Methods for diagnosing systemic lupus erythematosus

ABSTRACT

The present invention provides methods of diagnosing and monitoring systemic lupus erythematosus.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/743,289 filed May 12, 2022, which is a continuation of U.S.application Ser. No. 16/135,694 filed Sep. 19, 2018, issued as U.S. Pat.No. 11,360,099, which is a continuation of U.S. application Ser. No.13/992,086 filed Jul. 2, 2013, issued as U.S. Pat. No. 10,132,813, whichis a Section 371 US National Phase of International Application No.PCT/US2012/024729 filed Feb. 10, 2012, which claims priority to U.S.Application No. 61/441,785 filed Feb. 11, 2011, U.S. Application No.61/442,454 filed Feb. 14, 2011, and U.S. Application No. 61/472,424filed Apr. 6, 2011, all of which are incorporated by reference herein intheir entirety.

BACKGROUND OF THE INVENTION

Systemic lupus erythematosus (SLE) is an autoimmune disease,characterized by the production of unusual autoantibodies in the blood.These autoantibodies bind to their respective antigens, forming immunecomplexes which circulate and eventually deposit in tissues. This immunecomplex deposition causes chronic inflammation and tissue damage.

The precise reason for the abnormal autoimmunity that causes lupus isnot known. Inherited genes, viruses, ultraviolet light, and drugs mayall play some role. Genetic factors increase the tendency of developingautoimmune diseases, and autoimmune diseases such as lupus, rheumatoidarthritis, and immune thyroid disorders are more common among relativesof patients with lupus than the general population. Some scientistsbelieve that the immune system in lupus is more easily stimulated byexternal factors like viruses or ultraviolet light. Sometimes, symptomsof lupus can be precipitated or aggravated by only a brief period of sunexposure.

Since patients with SLE can have a wide variety of symptoms anddifferent combinations of organ involvement, no single test establishesthe diagnosis of SLE. To help doctors improve the accuracy of diagnosisof SLE, eleven criteria were established by the American RheumatismAssociation. These eleven criteria are closely related to the variety ofsymptoms observed in patients with SLE. When a person has four or moreof these criteria, the diagnosis of SLE is strongly suggested. However,some patients suspected of having SLE may never develop enough criteriafor a definite diagnosis. Other patients accumulate enough criteria onlyafter months or years of observation. Nevertheless, the diagnosis of SLEmay be made in some settings in patients with only a few of theseclassical criteria. Of these patients, a number may later develop othercriteria, but many never do. The eleven criteria conventionally used fordiagnosing SLE are: (1) malar over the cheeks of the face or “butterfly”rash; (2) discoid skin rash: patchy redness that can cause scarring; (3)photosensitivity: skin rash in reaction to sunlight exposure; (4) mucusmembrane ulcers: ulcers of the lining of the mouth, nose or throat; (5)arthritis: two or more swollen, tender joints of the extremities; (6)pleuritis/pericarditis: inflammation of the lining tissue around theheart or lungs, usually associated with chest pain with breathing; (7)kidney abnormalities: abnormal amounts of urine protein or clumps ofcellular elements called casts; (8) brain irritation: manifested byseizures (convulsions) and/or psychosis; (9) blood count abnormalities:low counts of white or red blood cells, or platelets; (10) immunologicdisorder: abnormal immune tests include anti-dsDNA or anti-Sm (Smith)antibodies, false positive blood tests for syphilis, anticardiolipinantibodies, lupus anticoagulant, or positive LE prep test, and (11)antinuclear antibody: positive ANA antibody testing.

Although the criteria serve as useful reminders of those features thatdistinguish lupus from other related autoimmune diseases, they areunavoidably fallible. Determining the presence or absence of thecriteria often requires interpretation. If liberal standards are appliedfor determining the presence or absence of a sign or symptom, one couldeasily diagnose a patient as having lupus when in fact they do not.Similarly, the range of clinical manifestations in SLE is much greaterthan that described by the eleven criteria and each manifestation canvary in the level of activity and severity from one patient to another.To further complicate a difficult diagnosis, symptoms of SLE continuallyevolve over the course of the disease. New symptoms in previouslyunaffected organs can develop over time. Because conventionally there isno definitive test for lupus, it is often misdiagnosed.

Monitoring disease activity is also problematic in caring for patientswith lupus. Lupus progresses in a series of flares, or periods of acuteillness, followed by remissions. The symptoms of a flare, which varyconsiderably between patients and even within the same patient, includemalaise, fever, symmetric joint pain, and photosensitivity (developmentof rashes after brief sun exposure). Other symptoms of lupus includehair loss, ulcers of mucous membranes and inflammation of the lining ofthe heart and lungs which leads to chest pain.

Red blood cells, platelets and white blood cells can be targeted inlupus, resulting in anemia and bleeding problems. More seriously, immunecomplex deposition and chronic inflammation in the blood vessels canlead to kidney involvement and occasionally failure requiring dialysisor kidney transplantation. Since the blood vessel is a major target ofthe autoimmune response in lupus, premature strokes and heart diseaseare not uncommon. Over time, however, these flares can lead toirreversible organ damage. In order to minimize such damage, earlier andmore accurate detection of disease flares would not only expediteappropriate treatment, but would reduce the frequency of unnecessaryinterventions. From an investigative standpoint, the ability touniformly describe the “extent of inflammation” or activity of diseasein individual organ systems or as a general measure is an invaluableresearch tool. Furthermore, a measure of disease activity can be used asa response variable in a therapeutic trial.

SUMMARY OF THE INVENTION

In a first aspect, the present invention provides methods for diagnosingsystemic lupus erythematosus (SLE), or monitoring SLE disease activitycomprising: (a) determining the level of at least one marker selectedfrom the group consisting of BC4d (B-lymphocyte-bound C4d), EC4d(erythrocyte-bound C4d), PC4d (platelet-bound C4d), and ECR1(erythrocyte complement receptor type 1) in a biological sample from thesubject; (b) determining the level of at least one further marker in abiological sample from the subject selected from the group consisting ofanti-MCV antibody marker and anti-nuclear antibody (ANA) marker; (c)calculating an SLE risk score by adjusting the level of one or more ofthe markers by one or more transformation analyses; (d) comparing theSLE risk score to a standard; and (e) one or more of: (I) diagnosing thesubject as having SLE based on the comparison; (II) determining a levelof SLE disease activity based on the comparison; (III) providing thecomparison to an entity for diagnosis of SLE; and (IV) providing thecomparison to an entity for monitoring SLE disease activity.

In one embodiment, steps (a) and (b) comprise determining a level of (a)EC4d marker and BC4d marker in a biological sample from a subject; and(b) one or both of anti-MCV antibody marker and anti-nuclear antibody(ANA) marker in a biological sample from the subject. In one embodiment,the method comprises determining a level of both the anti-MCV and theANA markers. In another embodiment, step (a) further comprisesdetermining a level of ECR1 in a biological sample from the subject.

In another embodiment, steps (a) and (b) comprise determining a level of(a) ECR1 and PC4d in a biological sample from a subject; and (b) ANAmarker in a biological sample from the subject.

In another embodiment, steps (a) and (b) comprise determining a level of(a) EC4d, BC4d, PC4d, and ECR1 in a biological sample from a subject;and (b) ANA marker in a biological sample from the subject. Thisembodiment may further comprise determining a level of anti-MCV antibodymarker in a biological sample from the subject.

In another embodiment of any of the above embodiments, or combinationsthereof, the method further comprises determining a level of doublestranded DNA antibody (anti-dsDNA) marker in a biological sample fromthe subject. In a further embodiment, determining the level of doublestranded DNA antibody (anti-dsDNA) marker in the biological sample iscarried out prior to determining the level of the other markers.

In various embodiments of any of the above embodiments, step (a)comprises determining a level of 2, 3, or all 4 of the recited markersin a biological sample from the subject.

In another embodiment, the one or more transformation analyses compriseslogistic regression analysis, and wherein the logistic regressionanalysis comprises (i) adjusting the level of one or more of the markersby an appropriate weighting coefficient to produce a weighted score foreach marker, and (ii) combining the weighted score for each marker togenerate the SLE risk score. In a further embodiment, the level of 2, 3,4, 5, or all markers is adjusted.

In yet another embodiment, calculating the SLE risk score comprises: (i)multiplying the amount of the markers by a predetermined weightingcoefficient to produce the weighted score for each marker; and (ii)summing the individual weighted scores to produce the SLE risk score.

In one embodiment, the biological sample comprises a blood sample. Inanother embodiment, determining the BC4d marker level comprisesdetermining the level of BC4d on the surface of B lymphocytes,determining the PC4d marker level comprises determining the level ofPC4d on the surface of platelets, and/or determining the EC4d and/orECR1 marker level comprises determining the level of EC4d and/or ECR1 onthe surface of erythrocytes. In a further embodiment, determining theBC4d marker level comprises determining the level of BC4d in a cell ortissue extract comprising B lymphocytes, determining the PC4d markerlevel comprises determining the level of PC4d in a cell or tissueextract comprising platelets, and/or determining the EC4d and/or ECR1marker level comprises determining the level of EC4d and/or ECR1 in acell or tissue extract comprising erythrocytes. In a still furtherembodiment, the level of the BC4d marker, the level of the PC4d marker,and/or the level of the EC4d marker is determined using an antibodyspecific for C4d.

The methods may further comprise contacting the biological sample withIgM, IgG and/or IgA rheumatoid factors, and/or anti-CCP antibodies underconditions suitable to promote specific binding of the antibodies totheir target antigen in the biological sample, removing unboundantibodies, detecting binding complexes between the antibodies and theirtargets in the biological sample, and comparing a level of such bindingcomplexes to a standard, wherein an increase in such binding complexesrelative to the standard indicates that the subject has rheumatoidarthritis.

In one embodiment, the method comprises providing the comparison to anentity for diagnosis of SLE. In another embodiment, the method comprisesproviding the comparison to an entity for monitoring SLE diseaseactivity. In a further embodiment, the method comprises diagnosing thesubject as having SLE based on the comparison. In another embodiment,the method comprises determining a level of SLE disease activity basedon the comparison.

In a second aspect, the present invention provides methods fordiagnosing systemic lupus erythematosus (SLE) comprising (a) determininga level of double stranded DNA antibodies (anti-dsDNA), in a biologicalsample from a subject; (b) determining a level of at least one markerselected from the group consisting of BC4d (B-lymphocyte-bound C4d),EC4d (erythrocyte-bound C4d), PC4d (platelet-bound C4d), and ECR1(erythrocyte complement receptor type 1) in a biological sample from thesubject; (c) calculating an SLE risk score by adjusting the level of oneor more of the markers by one or more transformation analyses; (d)comparing the SLE risk score to a standard; and (e) one or more of: (I)diagnosing the subject as having SLE based on the comparison; (II)determining a level of SLE disease activity based on the comparison;(III) providing the comparison to an entity for diagnosis of SLE; and(IV) providing the comparison to an entity for monitoring SLE diseaseactivity.

In one embodiment, the method comprises determining the level of doublestranded DNA antibody (anti-dsDNA) marker in the biological sample priorto determining the level of the one or more other markers in step (b).In various embodiments of any of the above embodiments, step (b)comprises determining a level of 2, 3, or all 4 of the recited markersin a biological sample from the subject.

In another embodiment, the one or more transformation analyses compriseslogistic regression analysis, and wherein the logistic regressionanalysis comprises (i) adjusting the level of one or more of the markersby an appropriate weighting coefficient to produce a weighted score foreach marker, and (ii) combining the weighted score for each marker togenerate the SLE risk score. In a further embodiment, the level of allmarkers is adjusted.

In another embodiment, calculating the SLE risk score comprises (i)multiplying the amount of the markers by a predetermined weightingcoefficient to produce the weighted score for each marker; and (ii)summing the individual weighted scores to produce the SLE risk score.

In one embodiment, the sample is a blood sample. In another embodiment,determining the BC4d marker level comprises determining the level ofBC4d on the surface of B lymphocytes, determining the level of PC4d onthe surface of B lymphocytes comprises determining the level of BC4d onthe surface of platelets, and/or determining the EC4d and/or ECR1 markerlevel comprises determining the level of EC4d and/or ECR1 on the surfaceof erythrocytes. In a further embodiment, determining the BC4d markerlevel comprises determining the level of BC4d in a cell or tissueextract comprising B lymphocytes, determining the PC4d marker levelcomprises determining the level of PC4d in a cell or tissue extractcomprising platelets, and/or determining the EC4d and/or ECR1 markerlevel comprises determining the level of EC4d and/or ECR1 in a cell ortissue extract comprising erythrocytes.

In another embodiment, the level of the BC4d marker, the PC4d, and/orthe level of the EC4d marker is determined using an antibody specificfor C4d. In a further embodiment, the methods further comprisecontacting the biological sample with ANA, anti-MCV, IgG and/or IgArheumatoid factors, and/or anti-CCP antibodies under conditions suitableto promote specific binding of the antibodies to their target antigen inthe biological sample, removing unbound antibodies, detecting bindingcomplexes between the antibodies and their targets in the biologicalsample, and comparing a level of such binding complexes to a standard,wherein an increase in such binding complexes relative to the standardindicates that the subject has rheumatoid arthritis.

In one embodiment, the method comprises providing the comparison to anentity for diagnosis of SLE. In another embodiment, the method comprisesproviding the comparison to an entity for monitoring SLE diseaseactivity. In a further embodiment, the method comprises diagnosing thesubject as having SLE based on the comparison. In another embodiment,the method comprises determining a level of SLE disease activity basedon the comparison.

In a third aspect, the present invention provides a non-transitorycomputer readable storage medium comprising a set of instructions forcausing a device for measuring marker levels in a biological sample tocarry out the method of any of the embodiment or combination ofembodiments of the methods of the inventions.

In a fourth aspect, the present invention provides a combination oftests comprising at least three of: (a) a first test for the level ofEC4d; (b) a second test for the level of BC4d; (c) a third test for thelevel of anti-MCV antibodies; (d) a fourth test for the level of ANA;and (e) a fifth test for the level of anti-dsDNA antibodies.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating an embodiment of the methods of theinvention for diagnosing SLE based on blood sample levels of cell-basedcomplement activation products containing three detection tiers.

FIG. 2 is a flow chart illustrating an embodiment of the methods of theinvention for diagnosing SLE based on blood sample levels of cell-basedcomplement activation products containing two detection tiers.

FIGS. 3A-3B are graphs demonstrating discriminating power of the EC4Dmarker. FIG. 3A: SLE vs. all others: area 0.846; sensitivity 76.4,specificity 80.3; criterion >7.15. FIG. 3B: SLE vs. other diseases: area0.814; sensitivity: 69.0; specificity: 82.7; criterion: >8.94.

FIGS. 4A-4B are graphs demonstrating discriminating power of the ECR1marker. FIG. 4A: SLE vs. all others: area 0.701; sensitivity 73.7,specificity 59.2; criterion ≤16.25. FIG. 4B: SLE vs. other diseases:area 0.619; sensitivity: 47.6; specificity: 64.5; criterion: ≤16.25.

FIGS. 5A-5B are graphs demonstrating discriminating power of the BC4Dmarker. FIG. 5A: SLE vs. all others: area 0.847; sensitivity 70.9,specificity 84.3; criterion >39.385. FIG. 5B: SLE vs. other diseases:area 0.806; sensitivity: 65.7; specificity: 83.0; criterion: >43.55.

FIG. 6 is a graph showing ROC are under the curve for the index valuecalculated from the logistic regression equation using the combinationof markers compared to that of dsDNA by univariate analysis.

FIG. 7 illustrates the relationship between the index value and theprobability to exhibit a diagnosis of SLE.

FIG. 8 is a graph demonstrating the distribution of index values bydiagnostic.

FIG. 9 is a graph illustrating the discriminative power of the index inthe whole cohort.

FIG. 10 is a graph illustrating the relationship between the index valueand the probability to exhibit a diagnosis of SLE.

FIGS. 11A-11C are a summary (FIGS. 11A-11B) and graph (FIG. 11C) ofprobability to have SLE using the model integrating serological markers(ANA, DsDNA and anti-MCV) together with C4D levels deposited onerythrocytes and B cells.

FIG. 12 is a graph demonstrating index value in dsDNA negative patients.

FIG. 13 is a graph demonstrating sensitivity and specificity as afunction of the index.

FIG. 14 is a flow chart illustrating an embodiment of the methods of theinvention for diagnosing SLE based on marker levels containing twodetection tiers.

FIG. 15 is a graph demonstrating the effect of the CB-CAPS component onthe index when ANA and Anti-MCV are equivocals.

DETAILED DESCRIPTION OF THE INVENTION

All references cited are herein incorporated by reference in theirentirety.

Within this application, unless otherwise stated, the techniquesutilized may be found in any of several well-known references such as:Molecular Cloning: A Laboratory Manual (Sambrook, et al., 1989, ColdSpring Harbor Laboratory Press), Gene Expression Technology (Methods inEnzymology, Vol. 185, edited by D. Goeddel, 1991. Academic Press, SanDiego, CA), “Guide to Protein Purification” in Methods in Enzymology (M.P. Deutshcer, ed., (1990) Academic Press, Inc.); PCR Protocols: A Guideto Methods and Applications (Innis, et al. 1990. Academic Press, SanDiego, CA), Culture of Animal Cells: A Manual of Basic Technique, 2^(nd)Ed. (R. I. Freshney. 1987. Liss, Inc. New York, NY), Gene Transfer andExpression Protocols, pp. 109-128, ed. E. J. Murray, The Humana PressInc., Clifton, N.J.), and the Ambion 1998 Catalog (Ambion, Austin, TX).

All embodiments disclosed herein can be combined with one or more otherembodiments in the same or different aspect of the invention, unless thecontext clearly indicates otherwise.

In a first aspect, the present invention provides methods for diagnosingsystemic lupus erythematosus (SLE), or monitoring SLE disease activitycomprising: (a) determining the level of at least one marker selectedfrom the group consisting of BC4d (B-lymphocyte-bound C4d), EC4d(erythrocyte-bound C4d), PC4d (platelet-bound C4d), and ECR1(erythrocyte complement receptor type 1) in a biological sample from thesubject; (b) determining the level of at least one further marker in abiological sample from the subject selected from the group consisting ofanti-MCV antibody marker and anti-nuclear antibody (ANA) marker; (c)calculating an SLE risk score by adjusting the level of one or more ofthe markers by one or more transformation analyses; (d) comparing theSLE risk score to a standard; and (e) one or more of: (I) diagnosing thesubject as having SLE based on the comparison; (II) determining a levelof SLE disease activity based on the comparison; (III) providing thecomparison to an entity for diagnosis of SLE; and (IV) providing thecomparison to an entity for monitoring SLE disease activity.

The present invention provides methods for the diagnosis and monitoringof disease activity and response to treatment in systemic lupuserythematosus (SLE) using panels of biomarkers. The inventorsdemonstrate herein that the methods of the invention provide specificityand sensitivity of SLE diagnosis and disease monitoring compared toprevious methods. For example, while the presence of anti-dsDNAantibodies has been used for diagnosing SLE and monitoring diseaseseverity, there is a large subset of confirmed SLE patients(approximately 40-50%) that test negative for anti-dsDNA antibodies.Thus, traditional measures for diagnosing and monitoring SLE in asubject lack accuracy and sensitivity, and improved methods of diagnosisand monitoring are needed.

The subject may be any one at risk of SLE (in methods for diagnosingSLE), or one known to have SLE (in methods for monitoring diseaseactivity), preferably a human subject (adult or pediatric). SLE is anautoimmune disease, characterized by the production of unusualautoantibodies in the blood. These autoantibodies bind to theirrespective antigens, forming immune complexes which circulate andeventually deposit in tissues. Symptoms of SLE include, but are notlimited to, malaise, fever, chronic inflammation, tissue damage; malarover the cheeks of the face or “butterfly” rash; discoid skin rash:patchy redness that can cause scarring; photosensitivity: skin rash inreaction to sunlight exposure, mucus membrane ulcers: ulcers of thelining of the mouth, nose or throat; arthritis: two or more swollen,tender joints of the extremities; pleuritis/pericarditis: inflammationof the lining tissue around the heart or lungs, chest pain withbreathing; hair loss; kidney abnormalities: abnormal amounts of urineprotein or clumps of cellular elements called casts; brain irritation:manifested by seizures (convulsions) and/or psychosis; blood countabnormalities; immunologic disorder: abnormal immune tests includeanti-dsDNA or anti-Sm (Smith) antibodies, false positive blood tests forsyphilis, anticardiolipin antibodies, lupus anticoagulant, and positiveLE prep test.

As used herein, the “biological sample” is obtained from the subject'sbody. Any suitable biological sample from the subject may be used.Particularly suitable samples for use in the methods of the inventionare blood samples, biopsy samples, including but not limited to kidneybiopsies. In one embodiment, serological markers (such as one or both ofanti-MCV antibody marker and an ANA marker) are obtained from a bloodsample, while EC4d, PC4d, ECR1, and/or BC4d markers are those depositedon circulating blood cells.

Blood samples are preferably treated with EDTA(ethylenediaminetetraacetate) to inhibit complement activation. Samplescan be maintained at room temperature or stored at 4° C. In someembodiments, a whole blood sample may be fractionated into differentcomponents. For instance, in one embodiment, red blood cells areseparated from other cell types in the sample by differentialcentrifugation. Analysis of complement activation products bound toerythrocytes (e.g., EC4d and ECR1) can be performed on the isolated redblood cells. In some embodiments, the white blood cells are isolatedfrom other components of the blood sample. For example, white bloodcells (the buffy coat) can be isolated from plasma and from red bloodcells by centrifugation. Each type of white blood cell (e.g. lymphocyte,monocyte, etc.) can be isolated through the use of antibodies againstknown cell surface markers that are specific for that cell type.Antibodies against cell surface markers of white blood cells are knownto those of skill in the art. For instance, monoclonal antibodiesspecific for cell surface markers CD3, CD4, CD8, and CD19 arecommercially available and can be used to select lymphocytes. Analysisfor complement activation products found on the surface of white bloodcells, such as BC4d, can be performed in an isolated fraction of whiteblood cells. The platelet fraction can be from other blood components toallow analysis of platelet-bound complement activation products, such asPC4d. Platelet isolation can be performed with methods known in the art,including differential centrifugation or immunoprecipitation usingantibodies specific for platelets (e.g., CD42b).

The level (e.g., quantity or amount) of a particular biomarker can bemeasured in the sample using a variety of methods known to those ofskill in the art. Such methods include, but are not limited to, flowcytometry, ELISA using red blood cell, platelet, or white blood celllysates (e.g., lymphocyte lysates), and radioimmunoassay. In oneembodiment, the determination of the level of C4d is made using flowcytometric methods, with measurements taken by direct or indirectimmunofluorescence using polyclonal or monoclonal antibodies specificfor each of the molecules. Each of these molecules can be measured witha separate sample (e.g., red blood cell-, white blood cell-, orplatelet-specific fractions) or using a single sample (e.g., wholeblood).

In one embodiment, steps (a) and (b) comprise determining a level of (a)EC4d marker and BC4d marker in a biological sample from a subject; and(b) one or both of anti-MCV antibody marker and anti-nuclear antibody(ANA) marker in a biological sample from the subject. In one embodiment,the method comprises determining a level of both the anti-MCV and theANA markers. In another embodiment, step (a) further comprisesdetermining a level of ECR1 in a biological sample from the subject.This embodiment is described in detail in the examples that follow, andshows significant diagnostic improvement over prior art methods.

In another embodiment, steps (a) and (b) comprise determining a level of(a) ECR1 and PC4d in a biological sample from a subject; and (b) ANAmarker in a biological sample from the subject. This embodiment isdescribed in detail in the examples that follow, and shows significantdiagnostic improvement over prior art methods.

In another embodiment, steps (a) and (b) comprise determining a level of(a) EC4d, BC4d, PC4d, and ECR1 in a biological sample from a subject;and (b) ANA marker in a biological sample from the subject. Thisembodiment may further comprise determining a level of anti-MCV antibodymarker in a biological sample from the subject. This embodiment isdescribed in detail in the examples that follow, and shows significantdiagnostic improvement over prior art methods.

In various embodiments of any of the above embodiments, step (a)comprises determining a level of 2, 3, or all 4 of the recited markersin a biological sample from the subject.

In various embodiments of any of the above embodiments, step (b)comprises determining a level of both the anti-MCV and the ANA markers.

In another embodiment of any of the above embodiments, or combinationsthereof, the method further comprises determining a level of doublestranded DNA antibody (anti-dsDNA) marker in a biological sample fromthe subject. In a further embodiment, determining the level of doublestranded DNA antibody (anti-dsDNA) marker in the biological sample iscarried out prior to determining the level of the other markers.

In a further embodiment that can be combined with any methods disclosedherein, step the methods further comprise determining a level of doublestranded DNA antibody (anti-dsDNA) marker in a biological sample fromthe subject. In this embodiment, the method may comprise determining thelevel of the (anti-dsDNA) marker in the biological sample prior todetermining the level of the other markers.

Methods for determining levels of EC4d, BC4d, PC4d, ECR1, ANA, andanti-MCV are disclosed throughout (such as in the Examples that follow)and are known in the art, while methods for determining the level ofanti-dsDNA are well known in the art, such as standard ELISAs. Anysuitable assay for determining the level of the markers may be used, asdiscussed below. In one embodiment, determining the marker levelcomprises determining the level of BC4d on the surface of B lymphocytes,determining the level of PC4d on the surface of platelets, and/ordetermining the level of and/or ECR1 on the surface of erythrocytes.Suitable assays for making such determinations are known in the art andinclude methods described herein. In one embodiment, flow cytometry isused.

In another embodiment, determining the marker level comprisesdetermining the level of BC4d in a cell or tissue extract comprising Blymphocytes, determining the level of PC4d in a cell or tissue extractcomprising platelets, and/or determining the level of EC4d and/or ECR1in a cell or tissue extract comprising erythrocytes. Suitable assays formaking such determinations are known in the art, and include ELISAassays of relevant cell extracts using antibodies specific for C4d.

The methods or any embodiment or combination of embodiments herein mayfurther comprise determining a ratio of one or more of EC4d and ECR1;BC4d and ECR1; and PC4d and ECR1. EC4D, PC4d, and BC4D tend to beelevated in SLE patients while ECR1 tends to be decreased. These ratioscan be used in combination with the methods of the invention to helppredict the likelihood of SLE. In one embodiment, the determination ofthe level of CR1 can be made using flow cytometric methods, withmeasurements taken by direct or indirect immunofluorescence usingpolyclonal or monoclonal antibodies specific for each of the molecules.

The methods described herein employ comparisons between a measured levelof a biomarker and a standard. Any suitable standard for comparison canbe used, including but not limited to a pre-determined level or rangefrom a normal individual or population of subjects suffering from SLE,or as otherwise described herein. As used herein, a “pre-determinedlevel” or “pre-determined range” refers to a value or range of valuesthat can be determined from the quantity or amount (e.g., absolute valueor concentration) of a particular biomarker measured in a population ofcontrol subjects (i.e. healthy subjects) or a population of subjectsafflicted with an autoimmune disease such as SLE, or a non-SLEautoimmune disorder. A pre-determined level or pre-determined range canbe selected by calculating the value or range of values that achievesthe greatest statistical significance for a given set of amounts orquantities for a particular biomarker. In some embodiments, thepre-determined level can be based on the variance of a sample ofbiomarker quantities from a population of control/normal subjects. Forinstance, the pre-determined level can be at least 2, 3, 4, or 5standard deviations above the normal range for a particular biomarker.In one embodiment, the pre-determined level is at least 6 standarddeviations above the normal range for the biomarker. In someembodiments, a pre-determined level or pre-determined range can be aratio of levels of two different biomarkers measured from all subjects(including SLE patients). A pre-determined level or pre-determined rangecan also be determined by calculating a level or range of biomarkerquantities for which greater than 50%, 60%, 70%, 75%, 80%, 85%, 90%, or95% of patients having a quantity of biomarker within that level orrange have SLE. Samples in which the level of biomarker does not fallwithin the pre-determined range or pre-determined level, may require themeasurement of an additional biomarker before a diagnosis of SLE can bemade.

As used herein, “transformation analyses” can be any suitablemathematical operation, including but not limited to generalized models(e.g. logistic regression, generalized additive models), multivariateanalysis (e.g. discriminant analysis, principal components analysis,factor analysis), and time-to-event “survival” analysis. In onepreferred embodiment, the one or more transformation analyses compriseslogistic regression analysis, and wherein the logistic regressionanalysis comprises (i) adjusting the level of one or more of the markersby an appropriate weighting coefficient to produce a weighted score foreach marker, and (ii) combining the weighted score for each marker togenerate the SLE risk score.

In various embodiments, the levels of one, two, three, four, five, ormore (where additional markers used) markers may be adjusted by anappropriate weighting coefficient.

As will be understood by those of skill in the art based on theteachings herein, weighting coefficients can be determined by a varietyof techniques and can vary widely. In one example of determiningappropriate weighting coefficients, multivariable logistic regression(MLR) is performed using the maker levels found within two groups ofpatients, for example, one with and one without SLE. There are severalmethods for variable (marker) selection that can be used with MLR,whereby the markers not selected are eliminated from the model and theweighting coefficients for each predictive marker remaining in the modelare determined. These weighting coefficients can then be, for example,multiplied by the marker level in the sample (expressed in any suitableunits, including but not limited weight/volume, weight/weight,weight/number packed cells, etc.) and then, for example, summed tocalculate an SLE risk score.

As used herein, “combining” includes any mathematical operation to usemarkers in combination to arrive at a single score that can be comparedto a threshold (adding, subtracting, dividing, multiplying, andcombinations thereof). In these methods, the level of 1, 2, 3, 4, 5, orall of the markers may be adjusted using an appropriate weightingcoefficient. Preferably, all markers are adjusted.

In a further embodiment, calculating the SLE risk score comprises (i)multiplying the amount of the markers by a predetermined weightingcoefficient to produce the weighted score for each marker; and (ii)summing the individual weighted scores to produce the SLE risk score.

In one embodiment, the methods comprise a single-tiered analysis, inwhich the risk factor is based on an index derived from a multivariatelogistic regression equation, in which the presence or absence (ordisease activity) of SLE is the classification variable and the markersare independent variables, one or more of them (1, 2, 3, 4, 5, all,etc.) associated with a coefficient. Examples of this embodiment areprovided in the examples that follow. In other embodiments, the methodscomprise a multi-tiered analysis. In one non-limiting embodiment, FIG.14 illustrates a multi-tier analysis method. In Tier 1 positivity fordsDNA is associated with a diagnosis of SLE. Among dsDNA negativepatients the index score composite of ANA, EC4d and BC4d levels measuredby fluorescence-activated cell sorting (FACS) and anti-MCV (by ELISA) iscalculated. An Index above a threshold is consistent with a diagnosis ofSLE. In one embodiment, the index score is calculated using ANAdetermined by ELISA. In another embodiment, indirect immunofluorescenceis performed when ANA is negative by ELISA.

In one embodiment, the methods may result in a diagnosis of the subjectas having SLE based on the comparison. In another embodiment, themethods may result in providing the comparison to an entity fordiagnosis of SLE. In these embodiments, the subject is at risk of SLEbut has not been definitively diagnosed with SLE. In variousembodiments, the subject may present with one or more symptoms of SLE,as described above. “Diagnosing/diagnosis,” as used herein, meansidentifying the presence or nature of SLE. Diagnostic methods differ intheir sensitivity and specificity. The “sensitivity” of a diagnosticassay is the percentage of diseased individuals who test positive(percent of “true positives”). Diseased individuals not detected by theassay are “false negatives.” Subjects who are not diseased and who testnegative in the assay, are termed “true negatives.” The “specificity” ofa diagnostic assay is 1 minus the false positive rate, where the “falsepositive” rate is defined as the proportion of those without the diseasewho test positive. While a particular diagnostic method may not providea definitive diagnosis of a condition, it suffices if the methodprovides a positive indication that aids in diagnosis.

In another embodiment, the methods may result in determining a level ofSLE disease activity based on the comparison. In a further embodiment,the methods may result in providing the comparison to an entity formonitoring SLE disease activity. In these embodiments, the methods canbe used, for example, to differentiate between subjects with activedisease and those with non-active disease, as demonstrated in theexamples that follow. For example, as shown in the examples that follow,the risk scores can be used to differentiate SLE patients with activedisease from those with non-active disease with high sensitivity andspecificity.

In these embodiments, the subject is one known to have SLE the method isused to determine a course of disease. In one embodiment, the subject isbeing treated for SLE. Such treatment regimens may include, but are notlimited to, immunosuppressants (ex: cyclophosphamide, corticosteroids,mycophenolate, etc.) and/or disease modifying antirheumatic drugs(DMARDs; ex: methotrexate, azathioprine, leflunomide, belimumab, andantimalarials such as PLAQUENIL® and hydroxychloroquine).

In another embodiment, the patient is in remission and the methodscomprise assessing recurrence, such as disease flares. SLE progresses ina series of flares, or periods of acute illness, followed by remissions.Disease-modifying antirheumatic drugs (DMARDs) are used preventively toreduce the incidence of flares, the process of the disease, and lowerthe need for steroid use; when flares occur, they are treated withcorticosteroids. In one embodiment, the methods of the invention canthus be used to, for example, monitor the efficacy of DMARDs in reducingflares. In an alternative embodiment, the methods can be used, forexample, to monitor the efficacy of steroids in treating flares. Themethods of the invention can thus be used to gauge disease activity,monitor and/or predict response to treatments, and monitor and/orpredict the onset of flares in SLE patients.

In one embodiment, the methods are in combination with SLEDAI scores (asare known in the art), to improve accuracy in monitoring SLE activityand/or in differentiating between active and non-active disease in asubject. An exemplary SLEDAI calculator that can be used in theseembodiments is shown below.

The methods may comprise determining a level of any other markers asdesired for a given purpose, together with modifying the SLE risk scorebased on a contribution of the additional markers. In one embodiment,the level of the one or more additional markers may be adjusted by oneor more transformation analyses

Any of the methods described herein can be used in combination withdifferential diagnostic assays. For example, antibodies directed againstanti-cyclic citrullinated peptide antibody (anti-CCP antibodies) arespecific serological markers for the diagnosis of rheumatoid arthritis(RA). In addition, antibodies to rheumatoid factor isotypes (RF IgM, IgAand IgG) are commonly used for the differential diagnosis of rheumaticdiseases. Thus, any of the methods of the invention may further comprisecontacting the sample with anti-CCP antibodies and/or IgM, IgG and IgArheumatoid factors under conditions suitable to promote specific bindingof the antibodies to their target antigen, removing unbound antibodies,detecting binding complexes between the antibodies and their targets inthe sample, and comparing a level of such binding complexes to astandard, wherein an increase in such binding complexes relative to thestandard indicates that the subject has RA. This embodiment helps serveto distinguish SLE from RA in the subject.

In a second aspect, the present invention provides methods fordiagnosing systemic lupus erythematosus (SLE) comprising: (a)determining a level of double stranded DNA antibodies (anti-dsDNA),EC4d, and BC4d markers in a sample from a subject; (b) calculating anSLE risk score by adjusting the level of one or more of the markers byone or more transformation analyses; (c) comparing the SLE risk score toa standard; and (d) one or more of: (I) diagnosing the subject as havingSLE based on the comparison; (II) determining a level of SLE diseaseactivity based on the comparison; (III) providing the comparison to anentity for diagnosis of SLE; and (IV) providing the comparison to anentity for monitoring SLE disease activity.

All embodiments and combination of embodiments disclosed in the firstaspect of the invention can be used in this second embodiment.Similarly, all common terms in this second aspect have the same meaningand embodiments disclosed for the first embodiment of the invention. Asdisclosed in the examples that follow, the inventors demonstrate thatthe methods of the invention provide specificity and sensitivity of SLEdiagnosis and disease monitoring compared to previous methods.

The methods may comprise determining a level of any other markers asdesired for a given purpose, together with modifying the SLE risk scorebased on a contribution of the additional markers. In one embodiment,the level of the one or more additional markers may be adjusted by oneor more transformation analyses. Such additional markers include, butare not limited to, platelet C4d (PC4d) and erythrocyte CR1 (ECR1). Theplatelet fraction is can be from other blood components to allowanalysis of platelet-bound complement activation products, such as PC4d.Platelet isolation can be performed with methods known in the art,including differential centrifugation or immunoprecipitation usingantibodies specific for platelets (e.g., CD42b). The methods may furthercomprise determining a ratio of one or more of EC4d and ECR1; BC4d andECR1; PC4d and ECR1. EC4D, PC4d, and BC4D tend to be elevated in SLEpatients while ECR1 tends to be decreased. These ratios can be used incombination with the methods of the invention to help predict thelikelihood of SLE. In one embodiment, the determination of the level ofCR1 can be made using flow cytometric methods, with measurements takenby direct or indirect immunofluorescence using polyclonal or monoclonalantibodies specific for each of the molecules.

In one embodiment, the methods comprise a two tiered analysis.Embodiments of a two tiered analysis are provided in the examples thatfollow. The method comprises a first tier of determining the level ofanti-dsDNA marker in a biological sample from the subject. If the levelof anti-dsDNA marker is within a predetermined level, then the subjectis diagnosed with SLE. However, if the level of anti-dsDNA marker isoutside the predetermined level, then a second tier analysis comprisesdetermining the level of EC4d, and BC4d markers in the sample. Thesubject is diagnosed with SLE if the level of the EC4d, and BC4d markersis within a predetermined level.

In another embodiment Tier 1 analysis involves both DS-DNA analysis andthe signal intensity of cell-based complement activation product(CB-CAP) markers, EC4D and BC4D (and optionally PC4d). See, for example,FIG. 2 . If the patient is positive on dsDNA testing or if the patienthas an “extreme threshold” for the intensity of EC4D, PCD4, or BC4D(i.e. has a complement-bound cellular signal that is in the range of atleast 6 standard deviations above the mean of that marker amongnon-lupus patients), then the patient is classified as positive for SLE.Patients that are negative on Tier 1 testing (i.e. negative for DS-DNA,which represents approximately 40-50% of all confirmed SLE patients, andhave no extreme threshold results in the CB-CAPS analysis) are thenevaluated in Tier 2. The Tier 2 analysis comprises determining a ratioof EC4D/ECR1 and/or BC4D/ECR1 to determine the SLE risk score. EC4D andBC4D tend to be elevated in SLE patients while ECR1 tends to bedecreased. These ratios can be used to predict the likelihood of SLE orSLE disease activity.

In another embodiment, the methods comprise a three tiered analysis (SeeExample 1). Embodiments of a three tiered analysis are provided in theexamples that follow. The method comprises determining the level ofanti-dsDNA marker in a biological sample from the subject. If the levelof anti-dsDNA marker is within a predetermined level, then the subjectis diagnosed with SLE. However, if the level of anti-dsDNA marker isoutside the predetermined level, then the level of EC4d and BC4d markers(and optionally platelet C4d (PC4d)) is determined in the sample. TheTier 2 analysis comprises determining whether the level or one, two, orall three of the EC4d, BC4d, and PC4d markers exceed an “extremethreshold” (i.e.: in the range of at least 6 standard deviations abovethe normal range for that marker). Subjects who have any of the EC4d,BC4d, and PC4d markers at or above the extreme level are declaredsuspect SLE patients at the specificity and diagnostic predictiveaccuracy established for Tier 2.

For the patient that does not exceed any of the extreme thresholds forthe tested markers, the method then goes to a Tier 3 analysis, whichcomprises determining a ratio of EC4D/ECR1 and/or BC4D/ECR1 to determinethe SLE risk score. EC4D and BC4D tend to be elevated in lupus patientswhile ECR1 tends to be decreased. These ratios predict the likelihood oflupus. Patients exceeding the established threshold for ratios aredesignated as suspect lupus patients at the specificity and diagnosticpredictive accuracy established for Tier 3.

The methods of all aspects of the invention as described herein can becarried out manually or may be used in conjunction with an automatedsystem or computer. For instance, the methods can be performed using anautomated system, in which a subject's blood sample is analyzed to makethe determination or determinations of levels of particular biomarkers,and the comparison with the pre-determined level or pre-determined rangeis carried out automatically by software appropriate for that purpose.Computer software, or computer-readable media for use in the methods ofthis invention include: a computer readable medium comprising: (a) codefor receiving data corresponding to a determination of complementcomponent C4d deposited on surfaces of red blood cells, platelets, orlymphocytes (e.g., B cells), and for data corresponding to an amount ofanti-MCV, ANA, and/or anti-dsDNA antibodies in the biological sample;(b) code for retrieving a pre-determined level for complement componentC4d deposited on surfaces of such cells of individuals, and forretrieving a predetermined level of anti-MCV antibodies, ANA, and/oranti-dsDNA antibodies is such samples; and (c) code for comparing thedata in (a) with the pre-determined level of (b) to make a determinationwhether an accurate SLE diagnosis can be made or whether additionalmeasurements of other biomarkers are required. In some embodiments, thecomputer readable medium further comprises (d) code for receiving datacorresponding to a determination of complement receptor CR1 deposited onsurfaces of red blood cells; (e) code for retrieving a pre-determinedlevel for complement receptor CR1 deposited on surfaces of red bloodcells of individuals; and (f) code for comparing the data in (d) withthe pre-determined levels of (e).

In certain embodiments of the invention, one or more pre-determinedlevels or pre-determined ranges of biomarker levels may be stored in amemory associated with a digital computer. After data corresponding to adetermination of complement C4d, anti-MCV antibodies, ANA, anti-dsDNAantibodies, and/or complement receptor CR1 is obtained (e.g., from anappropriate analytical instrument), the digital computer can compare themeasured biomarker data with one or more appropriate pre-determinedlevels or pre-determined ranges. After the comparisons take place, thedigital computer can automatically calculate if the data is indicativeof SLE diagnosis.

Accordingly, some embodiments of the invention may be embodied bycomputer code that is executed by a digital computer. The digitalcomputer may be a micro, mini or large frame computer using any standardor specialized operating system such as a Windows based operatingsystem. The code may be stored on any suitable computer readable media.Examples of computer readable media include magnetic, electronic, oroptical disks, tapes, sticks, chips, etc. The code may also be writtenby those of ordinary skill in the art and in any suitable computerprogramming language including, C, C++, etc.

Thus, the invention further comprises non-transitory computer readablestorage medium comprising a set of instructions for causing a device formeasuring marker levels in a sample to carry out the method of anyaspect or embodiment of the invention. In a further aspect, the presentinvention provides non-transitory computer readable storage media, forautomatically carrying out the methods of the invention on a computerlinked to a device for measuring levels of the recited markers in asample, such as a blood sample. As used herein the term “computerreadable medium” includes magnetic disks, optical disks, organic memory,and any other volatile (e.g., Random Access Memory (“RAM”)) ornon-volatile (e.g., Read-Only Memory (“ROM”)) mass storage systemreadable by the CPU. The computer readable medium includes cooperatingor interconnected computer readable medium, which exist exclusively onthe processing system or be distributed among multiple interconnectedprocessing systems that may be local or remote to the processing system.Any suitable device for measuring marker levels can be used, includingbut not limited to flow cytometry devices and devices for carrying ourELISAs.

The present invention also provides kits and combinations of tests fordiagnosing SLE. In one embodiment, the present invention includes acombination of tests useful for diagnosing SLE comprising at least threeof (i.e. 3, 4, or all 5): a first test for the level of EC4d, a secondtest for the level of BC4d, a third test for the level of anti-MCVantibodies, a fourth test for the level of ANA, and a fifth test for thelevel of anti-dsDNA antibodies. In some embodiments, the combinationfurther comprises at least one additional test for determining the levelof PC4d and/or ECR1. The kits or tests for determining the level ofparticular biomarkers include the various reagents for performing themeasurements according to the methods described herein. For instance, inone embodiment, the kits or tests include reagents for performingimmunofluorescence assays for each of the biomarkers, such as aconjugate of a monoclonal antibody specific for complement component C4dwith a fluorescent moiety, and in some embodiments, a conjugate of amonoclonal antibody specific for complement receptor CR1 with adifferent fluorescent moiety. In certain embodiments, the kits or testscan include reagents for detecting antinuclear or anti-dsDNA antibodies,such as secondary antibodies labeled with a fluorescent tag,chemiluminescent tag, radiolabel tag or the like. Additionally, the kitscan comprise such other material as may be needed in carrying out assaysof this type, for example, buffers, radiolabeled antibodies, colorimeterreagents, instructions for separating different cell fractions fromwhole blood, and instructions for diagnosing SLE based on particularpre-determined levels of the biomarkers.

In another embodiment, the kits or tests include reagents for performingother standard assays for each of the biomarkers, such as ELISA orradioimmunoassays. In such embodiments, the kits or tests comprisemonoclonal antibodies specific for C4d and CR1 conjugated withappropriate labels such as radioactive iodine, avidin, biotin or enzymessuch as peroxidase. The kits can additionally comprise buffers,substrates for antibody-conjugated enzymes, instructions for separatingdifferent cell fractions from whole blood, and instructions fordiagnosing SLE based on particular pre-determined levels of thebiomarkers.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All references, publications, patents, andpatent applications cited herein are hereby incorporated by reference intheir entireties for all purposes.

EXAMPLES Example 1

A Cell-Based Complement Activation Products Method for Diagnosing andMonitoring SLE

This Example outlines an embodiment of the methods of the invention toclassify a patient as having a high probability of suffering from SLE(or not) based on two pieces of diagnostic information: (1) presence orabsence of anti-double-strand DNA antibodies (DS-DNA) by a standardELISA assay; and (2) flow cytometric determination of cell-boundcomplement activation product levels (e.g., CB-CAPS assay). A flow chartdepicting the multi-step method is shown in FIG. 1 .

This multi-step approach involves three “tiers” of analysis. Tier 1analysis involves DS-DNA analysis alone. Given the high specificity ofdouble-strand DNA, a positive on DS-DNA is used to tentatively declare apatient positive for Lupus. CB-CAPS data for that patient is alsocollected for information related to monitoring and severity. A Tier 1positive patient is displayed as DS-DNA+, suspect Lupus, with CB-CAPSanalysis indicating whether the complement pattern is also consistentwith Lupus. A patient Tier 1 positive has a result displayed with aspecificity and diagnostic predictive accuracy value established forTier 1 positives.

In the event that a patient is negative on DS-DNA, which representsapproximately 40-50% of all confirmed SLE patients, then CB-CAPSanalysis is used to evaluate the patient in Tier 2. An “extremethreshold” approach was used to develop a series of signal intensitycut-offs using three cell-based complement activation product (CB-CAP)markers, EC4D, PC4D, and BC4D, that are used to further characterize theprobability of a patient being a lupus patient. Tier 2 analysisdetermines if any of the individual levels of the three CB-CAP markersexceeds the “extreme threshold.” The extreme threshold was developedempirically from normal (e.g., healthy subjects) and/or subjects with anautoimmune disease other than SLE, and is designed to recognize apatient who has a complement-bound cellular signal that is in the rangeof 6-7 standard deviations above the normal range for that marker.Patients who have any of the three cellular markers at the extreme levelare declared suspect lupus patients at the specificity and diagnosticpredictive accuracy established for Tier 2.

For the patient that does not exceed any of the extreme thresholds forthe three markers, the embodiment then goes to a Tier 3 analysis. InTier 3, a recursive partitioning approach was used to develop thresholdratios of the signal intensity for EC4D/ECR1 and BC4D/ECR1 to determinethe probability that a patient has lupus. EC4D and BC4D tend to beelevated in lupus patients while ECR1 tends to be decreased. Theseratios predict the likelihood of lupus. Patients exceeding theestablished threshold for ratios are designated as suspect lupuspatients at the specificity and diagnostic predictive accuracyestablished for Tier 3.

By using this step-wise approach, we are able to assign a diagnosticaccuracy value to the probability that a patient has lupus or not. Theconfidence level of a positive result is related to the level at whichthe diagnosis is made. At the first tier, the very high specificity ofDS-DNA gives a high confidence that this is in fact a lupus patient. Themethod can also use elevated levels of cell-bound complement to furtherenhance the confidence of this designation. At each Tier of analysis, apositive and negative predictive value is provided in the final report.The overall value of this approach is to provide as much information aspossible from diagnostic tests themselves, while maintaining as high aspecificity as possible during the diagnostic analysis.

Example 2

A Cell-Based Complement Activation Products Method for Diagnosing andMonitoring SLE

This Example outlines a second embodiment of the methods of theinvention to classify a patient as having a high probability ofsuffering from SLE (or not), and provides a demonstration, based on twopieces of diagnostic information: (1) presence or absence ofanti-double-strand DNA antibodies (DS-DNA) by a standard ELISA assay;and (2) flow cytometric determination of cell-bound complementactivation product levels (e.g., CB-CAPS assay). A flow chart depictingthe multi-step method is shown in FIG. 2 .

This multi-step approach involves two “tiers” of analysis. Tier 1analysis involves both DS-DNA analysis and the signal intensity of threecell-based complement activation product (CB-CAP) markers, EC4D, PC4D,and BC4D. If the patient is positive on dsDNA testing or if the patienthas an “extreme value” for the intensity of EC4D, PCD4, or BC4D (i.e.has a complement-bound cellular signal that is in the range of 6-7standard deviations above the mean of that marker among non-lupuspatients), then the patient is classified as positive for lupus. A Tier1 positive patient is displayed as either DS-DNA+, indicating a Lupusdiagnosis, or with CB-CAPS results indicating a Lupus diagnosis, orboth. A patient Tier 1 positive has a result displayed with aspecificity and diagnostic predictive accuracy value established forTier 1 positives.

Patients that are negative on Tier 1 testing (i.e. negative for DS-DNA,which represents approximately 40-50% of all confirmed SLE patients, andhave no extreme value results in the CB-CAPS analysis) are thenevaluated in Tier 2. In Tier 2, a recursive partitioning approach wasused to develop threshold ratios of the signal intensity for EC4D/ECR1and BC4D/ECR1 to determine the probability that a patient has lupus.EC4D and BC4D tend to be elevated in lupus patients while ECR1 tends tobe decreased. These ratios predict the likelihood of lupus. Patientswith ratios outside the bounds of established threshold ratios aredesignated as lupus patients at the sensitivity, specificity anddiagnostic predictive accuracy established for Tier 1 and Tier 2combined.

By using this step-wise approach, we are able to assign a diagnosticaccuracy value to the probability that a patient has lupus or not. Theconfidence level of a positive result is related to the tier at whichthe diagnosis is made. At the first tier, the very high specificity ofDS-DNA gives a high confidence that a DS-DNA positive patient is in facta lupus patient. The elevated levels of any of the cell-bound complementfurther enhance the confidence of a Tier 1 positive patient designationas a lupus patient. At both Tiers of analysis, a positive and negativepredictive value is provided in the final report. The overall value ofthis approach is to provide as much information as possible from thediagnostic tests themselves, while maintaining as high a specificity aspossible during the diagnostic analysis.

We have applied the 2-Tiered embodiment to the group of patientssummarized in Table 1 below.

TABLE 1 Study Subjects 589 208 SLE 145 ds DNA− Total  63 ds DNA+ Study381 All Others 202 Normal Healthy Subjects volunteers 179 OtherRheumatic Diseases

The SLE patients were diagnosed according to the ACR Criteria for theClassification of SLE. The Other Rheumatic Diseases included Rheumatoidarthritis (67%), systemic sclerosis (12%), dermatomyosistis (5%),Sjogren's (5%), other vasculitis (4%), polymyositis (4%), and others(3%). All 589 study subjects were tested for dsDNA using a standardELISA assay, and their levels of EC4D, BC4D, and PC4D were determined byflow cytometry, according to the Tier 1 strategy described above.

The results of Tier 1 are summarized in Table 2.

TABLE 2 Tier 1 Test Results Disease - Negative Disease - Positive Tier 1Test* n (%) n (%) Test + 13 (3.41) 103 (49.52) Test − 368 (96.59) 105(50.48) Total 381 208 *dsDNA Positive as defined by the manufacturer'sinstructions; extreme value of EC4D in Other Rheumatic Disease + NormalHealthy group is 5.65 + 6 * 3.327 = 25.612; extreme value for BC4D is28.26 + 6 * 32.535 = 223.47; extreme value for PC4D is 2.56 + 6 * 3.04 =20.8

The Sensitivity for Tier 1 testing is 50%; the Specificity is 97%.

The Tier 1—negative study subjects were analyzed in Tier 2. The valuesfor ECR1 were obtained by flow cytometry and the ratios of EC4D/ECR1 andBC4D/ECR1 were calculated. Recursive partitioning was used to establishthe following rule for a positive test result:

(EC4D/ECR1>0.59 and BC4D/ECR1>3.69) OR (EC4D/ECR1<=0.59 andBC4D/ECR1>4.48)=>Test (+)

The results of Tier 2 testing are summarized in Table 3.

TABLE 3 Tier 2 Test Results Disease - Negative Disease - Positive Tier 2Test* n (%) n (%) Test + 17 (4.62) 46 (43.81) Test − 351 (95.38) 59(56.19) Total 368 105 Sensitivity: 44%; Specificity: 95%

The results of the Tier 1 and Tier 2 analyses are summarized in Table 4.

TABLE 4 Summary of Two Tiered testing for Lupus diagnosis Tier 1Sensitivity 50% (N = 589) Specificity 97% Tier 2 (N = Sensitivity 44%473) Specificity 95% Overall Sensitivity 72% (N = 589) Specificity 92%

Example 3

This example outlines a third embodiment of the methods of the inventionwherein the likelihood of presenting with SLE as opposed to alternativerheumatic diseases is calculated based on an index derived from amultivariate logistic regression equation, in which the presence orabsence of SLE is the classification variable and the serologicalmarkers together with CB-CAPS fragments deposited on circulating cellsare independent variables, each of them associated with a coefficient.

In a study population of individuals comprising SLE patients, normalhealthy volunteers (NHV) and patients with other rheumatic diseases,CB-CAPS (EC4D, PC4D, BC4d, ECR1), ANA and dsDNA antibodies weredetermined. The study was a multicenter, cross-sectional study thatrequired one, or at most two, subject visits for screening and bloodsample collection. There were no follow-up visits required. After thesubject's informed consent was obtained, the following procedures wereperformed: The subject's medical history related to the diagnosis of anyand all rheumatologic conditions was obtained and reviewed forinclusion/exclusion criteria and details regarding the diagnosis ofthese conditions were collected. The date of diagnosis was recorded forSLE and other rheumatologic conditions, and the specific SLE diagnosticcriteria met was documented (revised ACR Criteria for the Classificationof SLE); Subject demographics was documented (date of birth, gender,race/ethnicity). A urine pregnancy test via dipstick was performed onall females of child-bearing potential. Approximately 15 mL of patient'sblood was obtained for analysis of CB-CAPS, dsDNA; the blood sample wasobtained under either fasting or non-fasting conditions. The sampleconsisted of one 4.5 ml-EDTA tube (lavender top), and one 7.5 ml SSTtube (red tiger top), which required centrifugation prior to shipping.All biological samples were sent by overnight delivery from the studysite to the clinical laboratory (using transportation kits provided).Because CB-CAPS should be analyzed within 48 hours of sample collection,samples were not accepted on Saturday; therefore, subjects were only beenrolled from Monday through Thursday (Thursday shipping cut-off time is10:00 a.m.). In order to preserve blinding in the analytical laboratory,case report forms and any subject information that would disclose thesubject's diagnosis were faxed to the clinical project manager, whereasblood samples for analysis were sent directly to the analyticallaboratory, accompanied by a completed subject-specific requisitionform. Results of these tests were not made available to the investigatorduring the conduct of the study. Specimens were identified only bysubject number and initials and the analytical lab remained blinded tosubject-specific diagnosis. Only the clinical team had access topatients' diagnoses throughout the study. Erythrocytes, B-lymphocytes,and platelets were isolated, washed, immunofluorescently labeled usingmonoclonal and/or polyclonal antibodies specific for CR1 and theCr-derived ligand C4d, and analyzed by flow cytometry using the assayvalidated in our clinical laboratory (see below section). Meanfluorescence intensity was used as an indicator of expression level ofeach cell surface marker; dsDNA were measured using an enzyme linkedimmunosorbent assay (ELISA, INOVA, San Diego Calif.).

Erythrocytes obtained from whole blood samples were tested using a panelof monoclonal antibodies to detect and measure cell surface levels ofC4d and CR1 complement activation products. Sample aliquots of patientwhole blood were diluted, washed, and stained with purified monoclonalantibodies against human C4d or CR1 (specific antibodies) and anon-specific antibody (MOPC-21 msIgG1k isotype control) for 45 minutesat 2-8° C. Samples were then washed and re-suspended in a solutioncontaining goat anti-mouse antibody conjugated to fluoresceinisothiocyanate (FITC) for 45 minutes at 2-8° C. (dark) to detect thecell surface bound monoclonal antibodies C4d or CR1. The cells were thenwashed and re-suspended in buffered saline for FACS analysis using aBeckman Coulter FC500 cytometer and CXP software. Light scatter (forwardand side) gating parameters during acquisition were used to isolate thelive erythrocytes for quantification of the non-specific and specificantibody binding (MFI) in the FL1 (FITC) channel. The MFI for theisotype background control (MOPC-21 msIgG1k) and each complement protein(C4d, CR1) from 20,000 events was obtained, and the net MFI was thendetermined by subtracting the non-specific MFI from the specific MFIresults. B-lymphocyte cells obtained from patient whole blood sampleswere tested using the C4d monoclonal antibody to measure cell surfacelevels of C4d by FACS. Sample aliquots from whole blood samples werelysed using BD Pharm Lyse™ lysing solution (ammonium chloride-basedlysing reagent) to remove red blood cells prior to staining with themonoclonal C4d antibody (45 minutes at 2-8° C.). Cell surface C4dstaining was detected using the goat anti-mouse fluoresceinisothiocyanate (FITC) antibody (45 minutes at 2-8° C., dark). Amonoclonal antibody against human CD-19 (CD-19 reacts with the 95 kDatype I transmembrane glycoprotein expressed during all stages of B-celldifferentiation and maturation) conjugated to R-phycoerythrin (R-PE) wasused to detect the C4d complement activation derived fragment specificto the B-lymphocytes. Stained cells were washed and re-suspended inbuffered saline for FACS analysis using a Beckman Coulter FC500cytometer using CXP software to isolate the B-lymphocytes cells and tomeasure fluorescent staining intensity. Light scatter (forward and side)gating parameters were used during acquisition to isolate alllymphocytes (150,000 live events) followed by secondary gating based onpositive CD-19 R-PE staining for analysis of the B-lymphocyte subset ofcells. Quantification of the non-specific (MOPC-21 msIgG1 isotypecontrol) and specific (C4d) fluorescence in the FL1 (FITC) channel wasdetermined for the gated B-lymphocyte cell subset. As above, net MFI foreach patient sample was determined by subtraction of the isotype controlbackground MFI results from the specific C4d MFI results on gatedB-lymphocyte cells only. Platelet cells obtained from patient wholeblood samples were tested using the C4d monoclonal antibody to measurecell surface levels of C4d by FACS as above. Unwashed whole bloodsamples were diluted and stained with the monoclonal antibody againsthuC4d (45 minutes at 2-8° C.), followed by staining with goat anti-mouseconjugated to FITC (45 minutes at 2-8° C., dark). A monoclonal antibodyagainst human CD-42b conjugated R-phycoerythrin (R-PE) to (plateletspecific marker) was used to identify the C4d complement activationderived fragment specific to the platelets. FACS analysis was performedusing a Beckman Coulter FC500 cytometer using CXP software to measurefluorescent staining intensity. Light scatter (forward and side) gatingparameters were used during acquisition to isolate the plateletpopulation followed by secondary gating based on positive CD-42b R-PEstaining (platelets) for analysis of the platelet subset of cells.Quantification of the non-specific (MOPC-21 msIgG1 isotype control) andspecific (C4d) fluorescence in the FL1 (FITC) channel was determined forthe gated platelet cells (5000 events). s above, net MFI was determinedby subtraction of the isotype control background MFI results from thespecific C4d MFI results on gated platelet cells only.

The Inclusion Criteria in the clinical study were the following: Abilityto read, understand, and sign the informed consent form; ≥18 years ofage; Agreement to and able to have blood sample collected, subjects withrheumatologic conditions in the following two categories (Diagnosed withSLE according to the revised ACR Criteria for the Classification of SLE,Diagnosed with one of the following rheumatologic disorders:Anti-Phospholipid Syndrome; fibromyalgia (ANA+ patients only); systemicsclerosis; rheumatoid arthritis, polymyositis; dermatomyositis; Wegenersgranulomatosus; polyarteritis nodosa; cryoglobulinemic vasculitis;leukocytoclastic vasculitis; other immunologic vasculitides; primarySjogren's Syndrome). In addition normal adult healthy individuals wereenrolled. The exclusion Criteria consisted of: for normal healthyvolunteers only: Based on the Principal Investigator's judgment,clinically significant, concurrent morbidity including cardiovascular,psychiatric, neurologic, gastrointestinal (e.g., gastric or duodenalulcers, inflammatory bowel disease, history of GI bleeds), metabolic,pulmonary (e.g., asthma, COPD), renal (including renal insufficiency),hepatic, hematologic, immunologic, endocrine (e.g., hypothyroidism,diabetes), active infection or history of chronic infectious disease(Hepatitis B or C or HIV), neoplastic disease, and/or history of weightloss surgery. Overt or laboratory evidence of primary immunodeficiencysyndromes Pregnant or lactating women. Participating centers wereencouraged to recruit an equivalent number of subjects with SLE andother rheumatologic conditions to ensure a balanced sample. Blood wasdrawn from each subject for CB-CAPS and dsDNA analyses. All data andblood samples submitted were kept strictly anonymous by the use ofsubject I.D. numbers. Each center was assigned a two digit site number,and each center assigned a secondary I.D. number. Each center wasresponsible for maintaining a list of study numbers and associatedsubject names at their site. All subject identifiers were removed fromany supporting documentation and all blood samples were identified onlyby subject I.D. numbers and initials. In addition, the Sponsor'sclinical laboratory was blinded to all subjects' diagnoses. Thelaboratory was responsible for data collection, data verification, andreporting of all data. All basic demographic data, medical history, anddocumentation of disease diagnostic criteria were collected on standardCase Report Forms (CRF). The logistic and sample treatment procedureswere provided below: The laboratory provided a transportation kit foreach enrolled subject, equipped with a coolant cartridge. Eachtransportation kit contained an EDTA (4.5 ml) tube, a red tiger SST tube(7.5 ml) and a transfer vial for collection of separated serum, ifapplicable (see below). A pre-printed airbill was also included in thetransportation kit. Upon receipt of the transportation kit, the coolantcartridge was removed and placed in the freezer until needed forshipping. Upon enrollment of the subject and collection of blood in theEDTA and red tiger SST tubes, the following procedure were implemented:The SST sample was allowed to clot naturally and completely(approximately 10 minutes) at room temperature, then centrifuged as soonas possible thereafter to avoid hemolysis of the RBCs. Following thecentrifugation, the serum was transferred to the serum collection vialprovided, or it was left in the SST tube, depending upon the site'ssample handling procedures. The EDTA blood tube, SST tube, and serumcollection vial were all be placed immediately into the transportationkit with the coolant cartridge and shipped overnight the laboratory. Dueto the time-sensitive nature of the CB-CAPS assay, blood samples werenot collected or shipped after 10 a.m. on Thursdays. All samples will beshipped (San Diego facility) via overnight delivery. Patients' bloodsamples were prepared for pick-up and shipping as soon as possible aftercollection. In the event of a delay between collection and packaging forshipping, samples were refrigerated at 2-8° C. All subject records wereidentified only by initials and assigned subject I.D. numbers. Subjects'names were not to be transmitted to the laboratory. The study physicianat each site kept a Master Subject List. Subjects, after having thestudy explained to them, gave voluntary and written informed consent andHIPAA authorization (in compliance with 21 CFR Parts 50 and 312) at thescreening visit before participating in any study-related procedures.Each subject read, and signed an informed consent and an HIPAAauthorization form after having an opportunity to discuss with the Studyphysician. All participating patients were aware that he/she maywithdraw from the study at any time. The Informed Consent statement andHIPAA authorization contained all of the elements and mandatorystatements as defined in the CFR. Signed copies of the informed consentand HIPAA authorization forms were given to the subject and bothdocuments were placed in the study physician study files. A uniquesubject identification number was assigned at the time that the subjectsigns the informed consent form. CRFs were completed and faxed to thelaboratory within 1 week of obtaining blood samples. The original CRFswere retained by the study site. All CRFs were completed in a neat,legible manner to ensure adequate interpretation of data. Black ink wasused to ensure clarity of all reproduced CRFs. All references tospecific subjects were made by use of initials and by assigned subjectnumber, not by name. Subject confidentiality was maintained by deletingall names (marked through with black marker) in any reports or recordssubmitted with the CRF. Any modification of previously entered data wasmade by striking through the original entry with a single line,initialing and dating the change, and entering the correct data. Use ofopaque correction fluid, correction tape, and highlighters wasprohibited. This study was conducted in compliance with the protocol andthe ICH guidelines for Good Clinical Practice. No adverse eventinformation was recorded for this study unless the event is related tothe blood draw itself. Approval by the Institutional Review Board (IRB)prior to the start of the study was the responsibility of the studyphysician.

Statistical analysis was conducted using the R software with logisticregression analysis. Receiver operating curves were used as appropriatefor each of the markers (univariate analysis) and also following thedetermination of an index value as the output of the multivariatelogistic regression equation.

Results: A total number of 613 individuals were enrolled in the studyfrom April to August 2010. A total of 15 sites participated. Thisconsisted of 213 patients with a diagnosis of Lupus (90% females), 206normal healthy volunteers (65% females) and 185 patients with otherrheumatic diseases (80% females). Mean age in patients with SLE was41±14 years (mean±SD), it was 41±13 years in normal healthy volunteers(mean±SD), and 57±13 in patients with other rheumatic diseases. Asignificant proportion of patients with other rheumatic diseasespresented with a diagnosis of Rheumatoid arthritis.

TABLE 5 Diagnosis Number Rheumatoid Arthritis 125 Systemic FibrosisLimited 13 Dermatomyositis 9 Other vasculitis 8 Primary Sjogren'sSyndrome 8 Systemic Fibrosis diffuse 8 Polymyositis 7 Fibromylagia(ANA+) 2 Wegener Granulomatosus 2 Antiphospholipid Syndrome 1 SystemicFibrosis/Sjogren's 1 Polyarteris Nodosa 1 All other diseases 185

Among the 613 individuals enrolled. 9 of them did not meet the inclusioncriteria (4 protocol violation; 2 patients erroneously enrolled, 3 forother reasons). Thus 603 individuals were evaluable for the analysis.The Table below highlights the average levels of dsDNA, EC4d, PC4d, BC4dand ECR1 in normal healthy individuals compared to those with SLE andother diseases.

TABLE 6 dsDNA levels: Results are expressed as mean CI 95%. DsDNAConfidence Means −95% Confidence +95% Normal Healthy 39 33 46 Otherdiseases 61 47 76 SLE 229 196 263 All individuals 113 99 128

TABLE 7 EC4D levels (Net MFI): Results are expressed as mean CI 95% EC4DConfidence Means −95% Confidence +95% Normal Healthy 5.3 4.6 6.1 Otherdiseases 6.4 5.8 7.0 SLE 17.4 15.0 19.8 All individuals 9.9 8.9 10.9

TABLE 8 ECR1 levels (Net MFI): Results are expressed as mean CI 95% ECR1Means Confidence −95% Confidence +95% Normal Healthy 20.7 19.6 21.7Other diseases 15.9 14.9 16.9 SLE 13.3 12.4 14.1 All individuals 16.616.0 17.2

TABLE 9 BC4d levels (Net MFI Results are expressed as mean CI 95% BC4DConfidence Means −95% Confidence +95% Normal Healthy 23.5 21.4 25.7Other diseases 34.2 26.8 41.6 SLE 96.9 82.6 111.3 All individuals 50.644.7 56.4

TABLE 10 PC4d levels (Net MFI Results are expressed as mean CI 95% PC4DMeans Confidence −95% Confidence +95% Normal Healthy 2.0 1.2 2.8 Otherdiseases 3.6 3.0 4.2 SLE 16.2 12.0 20.4 All individuals 7.5 5.9 9.1

As a first step we tested the discriminating power for each of thebiomarker (EC4D, ECR1, BC4d, PC4D) measured in the study. Results arepresented in FIGS. 3-5 for EC4d, ECR1 and BC4d respectively. Clinicalsensitivity and specificity for each of the marker including PC4D (usinga preset cutoff at 20 Net MFI to account for the stability of theanalyte during transportation) are given in the Table below andhighlights the performance of each individual marker by univariateanalysis.

TABLE 11 SLE vs All Others: SLE vs Other diseases Sens/Spec Sens/SpecEC4D (Net MFI) 78%/80% 69%/83% ECR1 (Net MFI) 74%/59   74%/48% BC4D (NetMFI) 71%/85% 66%/83% PC4D (>20U) (Net MFI) 18%/99% 18%/99%

In 595 individuals comprising 209 patients with SLE, 205 normal healthyvolunteers and 181 patients with other rheumatic diseases (121 of themwith a diagnosis of rheumatoid arthritis), CB-CAPS and DsDNA antibodieswere available for the multivariate logistic regression analysis. Thestudy population was randomly divided (using R function) in a trainingset comprising a total of 219 individuals (54 NHV, 101 SLE, and 64patients with other rheumatic diseases) to develop a modeldifferentiating SLE patients from NHV and other diseases. The modeldeveloped in the training set was subsequently validated in anindependent validation set of 384 individuals (112 SLE patients, 151 NHVand 121 other rheumatic diseases).

The multivariate linear logistic regression model was developed in thetraining set using the following initial predictors: positivity forDsDNA [cutoff at 301 unit as per manufacturer cutoff], EC4d (Net MFI),BC4d (Net MFI), ECR1 (Net MFI), PC4d (Net MFI). All Net MFI values werelog normalized. An index reflecting the relative contribution of eachbiomarker in the logistic regression model was calculated. The validityof the index was subsequently tested in the validation set ofindividuals with SLE or other diseases (including NHV) as describedabove. Receiver operating curves (ROC) and area under the curves weredetermined and the index cutoff optimizing sensitivity and specificitywas determined.

The multivariate logistic regression model from the training setrevealed that DsDNA, EC4D and BC4d contributed significantly to thedifferential diagnosis of SLE versus other diseases and NHV. ECR1 andPC4d levels were not significantly contributing (data not shown). Thefollowing logistic regression equation was determined:Index=−8.3919+1.4469*(DsDNA>301)+1.6194*log(EC4D)+1.4121*log(BC4D)

The Table below illustrates the coefficients, standard error and levelof significance for each of the independent variable/analyte included inthe model.

TABLE 12 Estimate Std. Error P value (Intercept) −8.3919 1.16846.84e−13*** DsDNA > 301TRUE 1.4469 0.6915 0.0364* log(EC4D) 1.61940.3916 3.54e−05*** log(BC4D) 1.4121 0.3305 1.94e−05***

As presented in the FIG. 6 there was significantly greater ROC AUC (andthus discriminating capabilities) for the index value calculated fromthe logistic regression equation described above (AUC=0.912) compared tothat of dsDNA by univariate analysis (AUC=0.783). An optimal cutoff of 0for the index value revealed the following clinical performances:

TABLE 13 Index < 0 Indicates Other Positivity for SLE SLE NHV diseasesNegative 22 52 49 Positive 76 2 10

Sensitivity, specificity and accuracy are presented below and highlightthe capability of the index to differentiate SLE patients from thosewith other diseases, or alternatively those NHV and with other diseases.

TABLE 14 SLE vs NHV + SLE vs Other Other diseases diseases Accuracy %83.9 79.6 Sensitivity % 77.6 77.6 Specificity % 89.4 83.1

The logistic regression model developed in the training set (andassociated optimal cutoff below or above 0) was subsequently testedindependently in the validation set (384 individuals comprising 112 SLEpatients, 151 NHV and 121 other rheumatic diseases).

As presented below the sensitivity, specificity and accuracy in thevalidation population were similar to those reported in the training setand illustrate the validity of the multivariate logistic regressionmodel developed.

TABLE 15 Index < 0 Indicates other Other Positivity for SLE SLE NHV RANon-RA disease Negative 20 139 37 50 87 Positive 91 12 24 9 33

TABLE 16 SLE vs NHV + SLE vs Other Other diseases diseases Accuracy %83.0 77.1 Sensitivity % 82.0 82.0 Specificity % 83.4 72.5

Altogether, the following performances (cutoff at index=0) could beestablished by combining the individuals from the training andvalidation sets (n=595).

TABLE 17 other Other SLE NHV RA Non-RA disease Negative 42 191 87 51 138Positive 167 14 34 9 43

Clinical sensitivity, specificity and accuracy are described below andhighlight the performances of the diagnostic method with a sensitivity,specificity and overall accuracy above 75%. ROC curve of the index valuecompared to dsDNA is presented in FIG. 4 .

TABLE 18 SLE vs NHV + SLE vs Other Other diseases diseases Accuracy %83.4 78.2 Sensitivity % 79.9 79.9 Specificity % 85.2 76.2

FIG. 7 illustrates the relationship between the index value and theprobability to exhibit a diagnosis of SLE.

Example 4

This example outlines a fourth embodiment of the methods of theinvention wherein the likelihood of presenting SLE as opposed toalternative rheumatic diseases is calculated based on an index derivedfrom a multivariate logistic regression equation, in which the presenceor absence of SLE is the classification variable and DsDNA and anti-MCVtogether with CB-CAPS fragments deposited on circulating cells areindependent variables, each of them associated with a coefficient.

Anti-MCV levels were measured as per manufacturer instruction in 593individuals enrolled in the study. Positivity for anti-MCV was observedin 43/210 patients with SLE (specificity of 79.5%), 5/205 normal healthyindividuals (specificity of 95%), 79/119 patients with RA (sensitivityof 66%) and 8/59 patients with other diseases than rheumatoid arthritis(specificity of 86.4%). Multivariate logistic regression analysisrevealed that anti-MCV positivity in combination with dsDNA, log EC4dand Log BC4d as indicated was contributing to the differential diagnosisof SLE versus other diseases.

Logistic regression coefficients in 388 patients were obtained (210lupus patients and 178 patients with other diseases including 119patients with rheumatoid arthritis).

TABLE 19 Const.B0 LOG_Ec4d LOG_Bc4d dsdna01 MCV01 Estimate −6.199 1.4671.013 1.258 −1.796 Standard 0.780 0.263 0.217 0.474 0.317 Error t(383)−7.950 5.583 4.672 2.657 −5.664 p-level 0.000 0.000 0.000 0.008 0.000−95% CL −7.732 0.950 0.587 0.327 −2.420 +95% CL −4.666 1.984 1.440 2.189−1.173 Wald's 63.205 31.173 21.826 7.059 32.077 Chi-square p-level 0.0000.000 0.000 0.008 0.000 Odds ratio 0.002 4.336 2.754 3.519 0.166 (unitch) −95% CL 0.000 2.587 1.798 1.387 0.089 +95% CL 0.009 7.269 4.2198.927 0.310 Odds ratio 1972.545 73.466 3.519 0.166 (range) −95% CL136.356 12.043 1.387 0.089 +95% CL 28535.170 448.176 8.927 0.310

Therefore, The logistic regression equation is as follow:Index=−6.199+1.2581*(DsDNA>301)+1.4670*log(EC4D)+1.0132*log(BC4D)−1.7962*(antiMCV>20)

TABLE 20 INDEX Confidence Confidence Diagnosis Means −95.000% +95.000%INDEX N SLE 1.94347 1.65717 2.229766 210 Normal −0.99169 −1.13042−0.852954 205 Healthy Other −1.18703 −1.38776 −0.986310 178 diseases AllGrps −0.01089 −0.18327 0.161479 593

The distribution of index values by diagnostic is presented in FIG. 8 .FIG. 9 illustrates the discriminative power of the index in the wholecohort. ROC curve AUC was 0.890. An index value above 0 was associatedwith a sensitivity of 81% (170 of 210) and a specificity of 86% in thewhole cohort of patients. FIG. 10 illustrates the relationship betweenthe index value and the probability to exhibit a diagnosis of SLE.Specificity against patients with other diseases is presented below:

TABLE 21 Normal Other All Healthy Other disease Rheumatoid othersindividuals diseases non RA arthritis Positive  55  23  32 16  16Negative 328 182 146 43 103 specificity 86% 89% 82% 72% 87%

Altogether these data demonstrate that the addition of anti-MCV canprovide significant improvement in the clinical performance of theindex. However, the overall improvement in the specificity against RApatients was at the expense to the overall specificity against thosewith other diseases but without RA.

TABLE 22 SLE vs Other Index: Index: diseases w/o antiMCV w antiMCVSensitivity 79.9% 80.9% Specificity 76.2% 82.0% Accuracy 78.2% 81.4%

The difference in specificity of the index in the 59 other non RAdiseases patients without (9 patients false positive) or with antiMCV(16 patients false positive) was 85% versus 72%. The difference wasrelated to 5 additional patients misclassified in the presence of theindex with anti-MCV (two patients with dermatomyositis, one patient withpolymyositis, one patient with primary Sjogren's syndrome and onepatient with diffuse systemic sclerosis).

The specificity for each of the other diseases enrolled in the study ispresented in the following Table:

TABLE 23 Index positive number With of antiMCV patients Specificity APS1 1 0% Vasculitis 3 8 63% Sjögren's Syndrome 2 8 75% Fibromyalgia (ANA+)0 2 100% Sjögren's Syndrome + vasculititis 0 1 100% Systemic Sclerosis −Diffuse 2 8 75% Systemic Sclerosis − Limited 3 13 77% RheumatoidArthritis 16 119 87% Polymyositis 2 7 71% Dermatomyositis 3 9 67%Wegeners Granulomatosus 0 2 100% Grand Total 32 178 82%

The present method for the differential diagnosis of SLE can alsointegrate various serological markers associated the differentialdiagnosis of other rheumatic diseases. For example, the diagnosis ofrheumatoid arthritis relies on the determination of rheumatoid factors(IgM, IgG, IgA). Therefore the determination of these serologicalmarkers described above will be integral to the discriminating power ofthe index we developed.

Example 5

This example outlines a fifth embodiment of the methods of the inventionwherein the likelihood of presenting SLE as opposed to alternativerheumatic diseases is calculated based on an index derived from amultivariate logistic regression equation, in which the presence orabsence of SLE is the classification variable and DsDNA, anti-MCV, andANA together with CB-CAPS fragments deposited on circulating cells areindependent variables, each of them associated with a coefficient.

ANA levels, DsDNA and AntiMCV levels were measured as per manufacturerinstruction in 593 individuals enrolled in the study. The percentage ofpatients positive for these markers is presented in the Table below.

TABLE 24 Anti-MCV ANA (% pos) DsDNA (% pos) (% pos) >20 units* >301units* >20 units* Normal Healthy 9.2% 0.5% 5.4% Other diseases 41.0%5.1% 49.4% SLE 88.5% 30.0% 20.5%

Logistic regression coefficients in 388 patients (210 lupus patients and178 patients with other diseases including 120 patients with rheumatoidarthritis).

TABLE 25 Std. Error z Estimate value p value (Intercept) −6.4477 1.18475.25e−08*** DsDNA > 301 1.0884 0.5085 0.0323* ANA > 20 2.2181 0.30081.65e−13*** LOG_EC4D 1.3072 0.2792 2.84e−06*** LOG_BC4D 0.9518 0.23093.75e−05*** Anti-MCV > 20 0.3413 −4.529 5.93e−06*** LOG_ECR1 −0.48660.275 0.0769 LOG_PC4D 0.1041 0.161 0.5181

The Table illustrates the incremental value of EC4D, BC4D and antiMCV tothe differential diagnosis of SLE versus other diseases.

TABLE 26 Model AUC (DsDNA > 301) 0.625 (DsDNA > 301) + (ANA > 20) 0.784(DsDNA > 301) + (ANA > 20) + LOG(EC4D) 0.868 (DsDNA > 301) + (ANA >20) + LOG(EC4D) + LOG(BC4D) 0.886 (DsDNA > 301) + (ANA > 20) +LOG(EC4D) + LOG(BC4D) + 0.910 MCV > 20)

Therefore, The logistic regression equation with the highest predictivevalue is as follow:Index=−6.150+0.996*(DsDNA>301)+1.480*(ANA>20)+1.422*log(EC4D)+0.876*log(BC4D)−1.883*(antiMCV>20).

The performance of the models on blinded subjects was estimated byrepeatedly designing models from a random subset of the data, applyingto the ‘blinded’ remainder, and compiling results. This technique, akinto in-silico validation, produces better estimates of performance thanthose that compute performance from classification of subjectspreviously used to design the models. In our analysis, 5,000 randomsubsets of 290 subjects each were used to generate models. Subsequentlyeach model was applied to the 98 blinded subjects. As measures ofperformance, sensitivity and specificity were computed. For the NHVsubjects, this was not necessary, as none of these subjects were used togenerate models.

The Table below illustrates the index value in those normal healthyvolunteers, those with other diseases and those with SLE.

TABLE 27 Average (CI95%) NHV −1.65 (−3.636; 0.338) Other diseases −1.49(−4.60; 1.62) SLE  2.14 (−1.99; 6.27)

The distribution of index values by diagnostic is presented in FIG. 10 .

TABLE 28 Model1 Model2 Model3 Model4 Model5 Sensitivity 31.1 89 79.579.5 83 specificity 92.7 57.9 72.3 77.5 80.3 (other diseases)Specificity 99.5 90.2 95.6 97.6 95.1 (NHV) Model1: (DsDNA > 301) Model2: (DsDNA > 301) + (ANA 22 20) Model 3: (DsDNA > 301) + (ANA > 20) +LOG(EC4D) Model 4: (DsDNA > 301) + (ANA > 20) + LOG(EC4D) + LOG(BC4D)Model 5: (DsDNA > 301) + (ANA > 20) + LOG(EC4D) + LOG(BC4D) + (MCV > 20)

The index value is in other diseases is presented in the Table below:

TABLE 29 Index N value St Dev Rheumatoid arthritis 120 −1.88 1.44Systemic Sclerosis 21 −0.82 1.56 Dermatomyositis 9 −0.23 1.11 Sjogren 8−0.19 1.05 Vasculitis 8 −0.72 2.53 Polymyositis 7 −0.38 1.27Fibromyalgia 2 −2.76 1.10 Wegeners Granulomatosus 2 −2.23 0.50 Sjogren +fibromyalgia 1 −0.58

FIGS. 11A-11C present the probability to have SLE using the presentmodel integrating serological markers (ANA, DsDNA and anti-MCV) togetherwith C4D levels deposited on erythrocytes and B cells. It also presentsa method to report the results.

It is understood that the disclosed invention is not limited to theparticular methodology, protocols and materials described as these mayvary. It is also understood that the terminology used herein is for thepurposes of describing particular embodiments only and is not intendedto limit the scope of the present invention which will be limited onlyby the appended claims.

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, many equivalents to the specificembodiments of the invention described herein. Such equivalents areintended to be encompassed by the following claims.

Example 6

The study of CB-CAPS (EC4d, BC4d), ANA, dsDNA and anti-MCV in subjectswith systemic lupus erythematosus versus Other Rheumatic Diseases andHealthy Normal Volunteers (referred to as “Capital”) was initiatedfollowing the analytical validation EC4d and BC4d. The objective of thisstudy was to establish the accuracy of the markers in discriminatingbetween subjects with SLE and those with other similar diseases in thestudy population described above in Example 3.

Statistical analysis was conducted using the R software with logisticregression analysis. Receiver operating curves were used as appropriatefor each of the markers (univariate analysis) and also following thedetermination of an index value as the output of the multivariatelogistic regression equation.

A total of 210 SLE patients (90.5% females, mean age 42 y), 178 patientswith other rheumatic diseases (80.3% females, mean age 57 y), and 205healthy individuals (65.8% females, mean age 41 y) participated fromApril to August 2010. All patients gave informed consent. The group ofpatients with other rheumatic diseases consisted mainly of rheumatoidarthritis patients (n=120, 67%) and patients with systemic sclerosis(n=21, 12%).

TABLE 30 Characteristics of the 210 SLE patients N (%) Gender (females)190 (90%) Race Caucasians 75 (36%) African Americans 76 (36%) Asians 16(8%) Hispanics 40 (19%) Others 3 (1%) Malar rash 91 (43%) Discoid rash29 (14%) Photosensitivity 76 (36%) Oral ulcers 59 (28%) Arthritis 154(53%) Serositis 59 (28%) Pleuritis 40 (19%) Pericarditis 26 (12%) Renaldisorder: 86 (41%) Proteinuria > 0.5 g/d 80 (38%) 3+ cellular casts 9(4%) Neurologic disorder: 15 (7%) Seizures 14 (7%) psychosis withoutother causes 2 (1%) Hematologic disorder: 113 (54%) Hemolytic anemia 8(4%) Leukopenia (<4000/L) 59 (28%) Lymphopenia (<1500/L) 53 (25%)Thrombocytopenia (<100,000/L) 29 (14%) Immunologic disorder: 171 (81%)anti-dsDNA 140 (67%) anti-Sm 47 (22%) anti-phospholipid 57 (27%)Antinuclear antibodies 205 (98%)

Univariate Analysis

Table 31 indicates the percentage positivity for serological markerstogether with EC4d, BC4d net mean fluorescence intensity (MFI) in eachof the three groups.

TABLE 31 Percentage positivity for ANA, anti-dsDNA, anti-MCV, EC4d, andBC4d Normal SLE Other diseases Healthy ANA ≥ 20 units 88.5% (186/210)41.0% (73/178) 9.3% (19/205) anti-dsDNA > 301 units (normal ≤ 301) 29.5%(62/210) 3.9% (7/178) 0.5% (1/205) Anti-MCV > 70 units (normal ≤ 70)1.9% (4/210) 36.0% (64/178) 0.5% (1/205) EC4d Net MFI (CI 95%) 17.6(15.2-20.0) 6.3 (5.7-6.8) 5.3 (4.6-6.1) BC4d Net MFI (CI 95%) 110.4(96.3-124.5) 34.9 (26.1-41.6) 23.5 (21.4-25.6)

ROC curves with SLE and other diseases patients (total of 388 patients)revealed that an EC4d level above 8.9 units (Net MFI) resulted in asensitivity of 70.0% and a specificity of 83% (AUC 0.825, CI95% 0.784 to0.862) against other diseases patients. Alternatively, BC4d levels above48 units (Net MFI) resulted in a sensitivity of 66% and a specificity of86% (AUC 0.822, CI95% 0.780 to 0.858) against other diseases patients.Among the 205 normal healthy individuals, 15/205 of them presented withan EC4d level above 8.9 units (93% specificity) while 9 others exhibitedBC4d levels above 48 units (96% specificity).

Multivariate Index Assay—Post Analytical Reduction of the Data

Anti-dsDNA was an insensitive (29.5%) yet highly specific (96%) markerfor SLE. The multi-step approach involved two “tiers” of analysis. Tier1 analysis involves DsDNA analysis alone in which positivity for dsDNA(>301 units) was associated with a diagnosis of lupus. In the secondtier analysis corresponding to 523 anti-anti-dsDNA negative individuals,a multivariate logistic regression analysis revealed that SLE wasassociated with ANA positivity (ANA≥20 units, p<0.001), anti-MCVnegativity (positivity for Anti-MCV>70 is affected with a negativecoefficient, p<0.001), and elevation of both log normalized (LOG) EC4dand BC4d net MFI (p<0.001) (ROC area=0.907). A summary of the resultsare presented in Table 32.

TABLE 32 Summary of Results of Multivariate Index Assay Estimate OddRatio P value (Intercept) −8.08   <2e−16 ANA ≥ 20 Units 2.2833 9.812.17e−14 AntiMCV > 70 units −2.6575 0.07 3.40e−05 LOG(EC4d) 1.1526 3.172.85e−05 LOG(BC4d) 1.1165 3.05 2.42e−06

An Index Equation corresponding to the output of the logistic regressionmodel follows:

Equation 1 Index equation in dsDNA negative patientsIndex=−8.08+2.2833×ANA20−2.6575×AntiMCV70+1.1526×log(EC4d)+1.1165×log(BC4d)

ANA20: If ANA levels are above 20 units, the result is entered as 1; ifANA<20 Units the result is entered as 0; AntiMCV70: If anti-MCV levelsare above 70 units, the result is entered as 1; if antiMCV≤70 Units theresult is entered as 0. Log corresponds to the natural log of net MFIfor EC4d and BC4d.

An example of index calculation form a patient enrolled in the CAPITALstudy is provided below.

TABLE 33 SLE Patient 05-011. DsDNA negative patient (75 units, <301units) Component Analyte Results index Index Calculation InterpretationANA 133 Units ANA20 = 1 = −8.080 + 2.2833 × 1 − Index > 0; AntiMCV <20units AntiMCV70 = 0 2.6575 × 0 + 1.1526 × 3.632 + Consistent ECd4  37units logEC4d = 3.632 1.1165 × 4.627 = with SLE BC4d 102 units logBC4d =4.627 3.54

An Index score (see FIG. 12 ) corresponding to a weighted sum of thesefour markers was 1.20 (CI95%: 0.86; 1.53) in SLE, −2.53 (CI95%: −2.83;−2.24) in other rheumatic diseases, and −2.74 (CI95%: −2.89; −2.59) innormal healthy volunteers (NHV). Sensitivity was 71.6%, specificityagainst other diseases was 90.1% patients and 97.6% against normalhealthy (Table 34).

A combination of anti-dsDNA positivity and the Index score (using acutoff of zero) yielded 80.0% sensitivity for SLE, and 86.6% specificityin distinguishing SLE from other rheumatic diseases (97.1% specificityin distinguishing SLE from healthy subjects) (see Table 34). FIG. 13illustrates the sensitivity and specificity (vs. other diseases) at anygiven index value.

TABLE 34 Clinical performances: Combination of Anti-dsDNA Positivity +Index Score Nb Other N positives SLE diseases Normals dsDNA (tier 1) 59370  62/210 (29.5%)  7/178 (3.9%) 1/205 (0.5%) Index (tier 2) 523 128106/148 (71.6%) 17/171 (9.9%) 5/204 (2.4%) TOTAL 593 198 168/210 (80.0%)24/178 (13.4%) 6/205 (2.9%) N = number

FIG. 14 illustrates the Tier analysis method. In Tier 1 positivity fordsDNA (levels>301 units) is associated with a diagnosis of SLE. AmongdsDNA negative patients the index score composite of ANA (by ELISA,cutoff at 20 units), EC4d and BC4d levels measured by FACS (Net MFI) andanti-MCV (by ELISA, cutoff at 70 Units) is calculated. An Index above 0is consistent with a diagnosis of SLE. While the index score iscalculated using ANA determined by ELISA, Indirect immuno-fluorescenceis performed when ANA is negative by ELISA. The specificity againstpatients with other rheumatic diseases is presented in Table 35 andranged from 63% to 100%.

TABLE 35 Specificity against patients with other rheumatic diseasesTier1 dsDNA Tier2 Total Diagnosis N positive Index > 0 PositiveSpecificity Rheumatoid arthritis 120 6 3 9 93% Scleroderma 21 1 4 5 76%Dermatomyositis 9 0 3 3 67% Vasculitis 8 0 3 3 63% Sjogren's 8 0 2 2 75%Polymyositis 7 0 2 2 71% Wegeners 2 0 0 0 100% GranulomatosusFibromyalgia 2 0 0 0 100% Sjogren + 1 0 0 0 100% fibromyalgia N = number

Moreover, as presented in Table 36 the addition of EC4d, BC4d andanti-MCV increased the AUC from 0.808 (dsDNA+ANA) to 0.918(dsDNA+ANA+EC4d+BC4d+antiMCV).

TABLE 36 Performances characteristics dsDNA + dsDNA + ANA + dsDNA +ANA + EC4d + dsDNA + ANA + EC4d + BC4d + ANA EC4d BC4d antiMCV SLEPositive 187 159 163 168 SLE Negative 23 51 47 42 Normal healthypositive 20 6 3 5 Normal healthy negative 185 199 202 200 Other diseasespositive 74 41 31 24 Other diseases negative 104 137 147 154 Sensitivity(%) 89 75.7 77.6 80 Specificity, Other 58.4 77 82.6 86.5 diseases (%)Specificity Normal 90.2 97.1 98.5 97.6 healthy (%) AUC 0.808 0.887 0.9030.918

The day to day reproducibility of the Index were also determined in atotal of 23 samples from 11 patients with SLE enrolled in the analyticalvalidation study (erythrocytes and B lymphocyte C4d levels). None ofthese patients were part of the clinical validation study. The Index wasdetermined 4 consecutive times on 4 consecutive days. The averagestandard deviation was 0.15 (range 0.04 to 0.31).

Equivocal Index Results Based on ANA and Anti-MCV Equivocal Values

Because the index cumulates two components (ANA and antiMCV) associatedwith a cutoff value, the index can potentially change from positive tonegative (or vice versa) based on the analytical error at the medicaldecision limit for ANA (20 units) or anti-MCV (70 units). For example,in Table 37 the index can changes from −0.28 (case 1, ANA negative) to+2.00 (case 2, ANA positive) based on two unit difference in ANA at thedecision limit (19 vs 21 Units)

TABLE 37 Effect of ANA cutoff on Index EC4d BC4d ANA AntiMCV Net NetAnti Units Units MFI MFI ANA20 MCV70 INDEX PATTERN Case1 19 10 10 100 00 −0.28 NON SLE Case2 21 10 10 100 1 0 2.00 SLE

It follows that equivocal results for the Index are preferably bedefined when ANA and/or anti-MCV levels are near the cutoff value (andtherefore potentially able to affect the positivity or negativity of theindex). We defined an equivocal ANA when ANA levels ranged from 16 to 24Units (20% CV at the 20 Units cutoff) and an equivocal anti-MCV whenanti-MCV levels ranged from 56 to 84 units (20% CV at the 70 Unitscutoff).

As presented in the FIG. 15 the change in the index value from positiveto negative is also heavily dependent on the CBCAPS component in themulti-index assay equation. A decision rule based on equivocal resultsfor ANA and anti-MCV was established (Table 38). In the CAPITAL study atotal of 15/523 patients dsDNA negative patients presented with anequivocal result (2.8%: 3 SLE, 3 other diseases and 7 normals). Whenthese patients were excluded from the analysis the sensitivity was 72.0%(103/143), specificity against other diseases was 90.5% patients(152/168) and 98.4% (194/197) against normal healthy analysis (see Tier2 analysis in Table 34 for comparison). While on a population basisthese sensitivity and specificity values are similar, the results willbe reported as equivocal on a per patient basis.

TABLE 38 Definition of Equivocal Results 16 < ANA 16 ≤ ANA ≤ 24 ANA > 24units units units AntiMCV < 56 NOT Equivocal if CBCAPS NOT unitsEQUIVOCAL component between 5.80 EQUIVOCAL and 8.08 56 ≤ AntiMCV ≤ 84Equivocal if CBCAPS Equivocal if CBCAPS Equivocal if component betweencomponent between CBCAPS component 8.08 and 10.74 5.80 and 10.74 between5.80 and 8.45 AntiMCV > 84 NOT Equivocal if CBCAPS NOT EQUIVOCALcomponent between EQUIVOCAL 8.45 and 10.74

Equivocal Zone Based on Analytical Error at the Index of Zero

The accuracy at the medical decision limit (index of 0), and thedefinition of an equivocal zone was established at the 95% confidenceinterval based on 1.96 time the average standard deviation observed inthe validation samples reported above (0.15). This corresponds to anequivocal zone of −0.3 to 0.3.

Table 39 indicates the performances characteristics of the index (Tier2, dsDNA negative patients) in all patients compared to the performanceswithout equivocal. As expected higher performance characteristics wereachieved without equivocal results.

TABLE 39 Performances characteristics of the tier 2 analysis withequivocals Specificity Tier 2 Sensitivity Other Specificity dsDNAnegative N SLE diseases Normals All 523 106/148 154/171 199/204  (100%)(71.6%) (90.0%) (97.5%) Without equivocals based 508 103/143 152/168194/197 on ANA and anti-MCV (97.1%) (72.0%) (90.4%) (98.5%) cutoffsWithout equivocals based 482  99/130 148/160  6/205 on ANA and anti-MCV(92.2%) (76.1%) (92.5%) (99.0%) cutoffs + error at Index = 0

A total of 8.8% patients presented either an equivocal result based onthe uncertainty at the medical decision limit for ANA and antiMCV. Aspresented in the patient report enclosed, the performancecharacteristics reported are those calculated from the whole population(inclusive of equivocal index results based on equivocal ANA andAnti-MCV) A cautionary note is added on the patient report andstipulates that the results should be interpreted with caution if theIndex value is comprised between −0.3 and 0.3.

Validation Study

The Diagnostic method was validated prospectively in an independentcohort of patients with SLE and other rheumatic diseases. The study wasin collaboration with the Lupus Center of Excellence (Pittsburgh, PAunder Susan Manzi, MD and Joseph Ahearn, MD). None of the patientsenrolled at the center were part of the patients enrolled in the CAPITALstudy.

After the subject's informed consent was obtained blood was obtained foranalysis of CB-CAPS, dsDNA, and ANA; The sample consisted of one 10ml-EDTA tube (lavender top), and one 5 ml SST tube (goldtop), whichrequired centrifugation prior to shipping. All biological samples weresent by overnight delivery from the Lupus Center of Excellence site toExagen Diagnostics (using transportation kits provided). Because CB-CAPSshould be analyzed within 48 hours of sample collection, samples werenot accepted on Saturday; therefore, subjects were only enrolled fromMonday through Thursday. In order to preserve blinding in the analyticallaboratory, CRFs and any subject information that would disclose thesubject's diagnosis were not provided to the laboratory. Specimens wereidentified only by subject number and initials and the analytical labremained blinded to subject-specific diagnosis. Erythrocytes andB-lymphocytes were isolated, washed, immunofluorescently labeled usingmonoclonal and/or polyclonal antibodies specific for Cr-derived ligandC4d, and analyzed by flow cytometry using the assay validated in ourclinical laboratory (see above section). Mean fluorescence intensity wasused as an indicator of expression level of each cell surface marker;dsDNA, ANA and antiMCV were measured using an enzyme linkedimmunosorbent assay (ELISA).

Results

From Jun. 8, 2011 to Sep. 30, 2011 a total of 52 patients were enrolledin the validation study. This consisted of 36 patients with SLE, and 16patients with other rheumatic diseases (among them 7 patients presentedwith rheumatoid arthritis, and 5 patients had Primary Sjogren'ssyndrome). Table 40 highlights the performances of the serologicalmarkers and CB-CAPS.

TABLE 40 Comparison between SLE Patients with Other Rheumatic DiseasesSLE Other diseases ANA ≥ 20 units 72% (26/36) 50% (8/16) anti-dsDNA >301 units 22% (8/36) 6% (1/16) (normal ≤ 301) Anti-MCV > 70 units 3%(1/36) 18% (3/16) (normal ≤ 70) EC4d Net MFI (CI 95%) 14.0 (9.5-18.5)5.2 (3.9-6.6) BC4d Net MFI (CI 95%) 53.4 (35.4-71.4) 21.3 (14.8-27.8)EC4d > 8.9 Units 50% (18/36) 6% (1/16) BC4d > 48 Units 36% (13/36) 6%(1/16)

Anti-dsDNA was an insensitive (22%, 8 positives) yet highly specific(94%) marker for SLE. The multi-step approach involving the two “tiers”as developed above was applied. The index score in dsDNA negativepatients was −0.22 (CI 95%: −1.28; 0.84) in SLE (28 patients) and −2.63(CI95%: −3.86; −1.41) in other rheumatic diseases (15 patients). Thecombination of anti-dsDNA positivity and the Index score (using a cutoffof zero) yielded 67% sensitivity for SLE, and 88.0% specificity indistinguishing SLE from other rheumatic diseases (see Table 41). Thissensitivity was not significantly different from the sensitivityobserved in the CAPITAL study (81%; p=0.117). Similarly, specificitiesbetween the two studies were identical (86.5% vs. 87.5%; p=1).

TABLE 41 Specificity in distinguishing SLE from other rheumatic diseasesOther N Nb positives SLE diseases dsDNA (tier 1) 52 9  8/36 (22.2%) 1/16(6.3%) Index (tier 2) 43 17 16/28 (57.1%) 1/15 (6.3%) TOTAL 52 26 24/36(66.7%) 2/16 (12.5%) Note one patient (number 111522) with SLE presentedan equivocal result. ANA was 23 Units and the Index value was −2.23Units (non SLE).

Note one patient (number 111522) with SLE presented an equivocal result.ANA was 23 Units and the Index value was −2.23 Units (non SLE).

As presented in Table 42 the addition of EC4d, BC4d and anti-MCVincreased the AUC from 0.588 (dsDNA+ANA) to 0.762(dsDNA+ANA+EC4d+BC4d+antiMCV).

TABLE 42 Performances Characteristics dsDNA + dsDNA + ANA + dsDNA +ANA + EC4d + dsDNA + ANA + EC4d + BC4d + ANA EC4d BC4d antiMCV SLEPositive 26 24 23 24 SLE Negative 10 12 13 12 Other diseases positive 84 1 2 Other diseases negative 8 12 15 14 Sensitivity (%) 72.2 66.7 63.966.6 Specificity (%) 50.0 75.0 93.8 87.5 AUC 0.588 0.679 0.731 0.762

Overall Clinical Performances

Altogether, the following performance characteristics can be derivedwhen the CAPITAL study is combined with the validation study:

TABLE 43 SLE Other diseases ANA ≥ 20 units 87% (213/246) 41% (81/194)anti-dsDNA > 28% (70/246) 4% (8/194) 301 units (normal ≤ 301) Anti-MCV >70 units 2% (4/246) 34% (67/194) (normal ≤ 70) EC4d > 8.9 Units 67%(165/246) 16% (32/194) BC4d > 48 Units 61% (151/246) 13% (25/194)

The combination of anti-dsDNA positivity and the Index score (using acutoff of zero) yielded 78% sensitivity for SLE, and 86.6% specificityin distinguishing SLE from other rheumatic diseases (97.1% specificityin distinguishing SLE from healthy subjects) (Table 44).

TABLE 44 Combination of anti-dsDNA positivity and Index score (using acutoff of 0) Nb Other N positives SLE diseases Normals dsDNA (tier 1)645 79  70/246 (28.45)  8/194 (4.1) 1/205 (0.5%) Index (tier 2) 566 145122/176 (69.3%) 18/186 (9.6%) 5/204 (2.4%) TOTAL 645 224 192/246 (78.0)26/194 (13.4%) 6/205 (2.9%)

As presented in Table 45 the addition of EC4d, BC4d and anti-MCVincreased the AUC from 0.787 (dsDNA+ANA) to 0.893(dsDNA+ANA+EC4d+BC4d+antiMCV).

TABLE 45 Performances Characteristics dsDNA + dsDNA + ANA + dsDNA +ANA + EC4d + dsDNA + ANA + EC4d + BC4d + ANA EC4d BC4d antiMCV SLEPositive 213 183 186 192 SLE Negative 33 63 60 54 Normal healthypositive 20 6 3 5 Normal healthy negative 185 199 202 200 Other diseasespositive 82 45 32 26 Other diseases negative 112 149 162 168 Sensitivity(%) 86.6 74.4 75.6 78.0 Specificity Other 57.7 76.8 83.5 86.6 diseases(%) Specificig Normal 90.2 97.1 98.5 97.6 healthy (%) AUC 0.787 0.8590.875 0.893

The overall specificity in other rheumatic diseases (n=194) is presentedin Table 46 and ranged from 56% to 100%.

TABLE 46 Specificity against patients with other rheumatic diseasesTier1 dsDNA Tier2 Total Diagnosis N positive Index>0 PositiveSpecificity Rheumatoid arthritis 127 6 3 9 93% Scleroderma 22 1 4 5 77%Dermatomyositis 10 0 3 3 70% Vasculitis 9 1 3 4 56% Sjogren's 13 0 3 377% Polymyositis 7 0 2 2 71% Weners granulomatosus 3 0 0 0 100%Fibromyalia 2 0 0 0 100% Sjogren + fibromyalia 1 0 0 0 100% All 194 8 1826 87% N = number

Example 7

Contribution of Serological and CB-CAPs to Active Disease

Disease activity was measured at the time of the study visit using theSafety of Estrogens in Lupus Erythematosus National Assessment (SELENA)version of the SLE Disease Activity Index (SLEDAI) in all SLE patients.A total of 41 SLE patients (19.6%) presented with active disease asassessed using a SLEDAI≥6. Patients presenting active disease hadelevated levels of ANA, EC4d, BC4d, PC4d and reduced levels of ECR1(p<0.003). ROC analysis indicated that ANA above 90 units (AUC=0.696)was associated with a 4.0 fold (CI95%: 1.8-8.8) higher likelihood ofactive disease. Similarly, EC4d above 14.8 units (ROC AUC=0.647), BC4dabove 71.5 units (ROC AUC=0.645) and PC4d above 6.3 units (ROCAUC=0.720) were associated with a 3.4 fold (CI95%: 1.6-7.0), a 4.3 fold(CI95%: 1.9-9.7) and 5.4 fold (CI95%: 2.4-12.1) greater likelihood ofactive disease, respectively. Alternatively, ECR1 below 10 net WI(AUC=0.690) were associated with a 4.1 fold (CI95%: 2.0-8.5) higherlikelihood of active disease. Moreover, the index score calculated todifferentiate SLE from other diseases was significantly higher in SLEpatients presenting with active disease than those with non activedisease. ROC analysis revealed that a cutoff of 1.36 on the indexdifferentiated SLE patients with active from those with non activedisease with a of 90.2% sensitivity and 54% specificity (46% falsepositives). Multivariate logistic regression indicated that ANA levels,ECR1 and PC4d levels contributed to active disease.

The activity score as the weighed sum of ANA, ECR1 and PC4d (using theestimates from the logistic regression analysis) was −2.05±1.13 inpatients with non active disease and −0.67±1.42 in patients with activedisease. ROC analysis indicated that an activity score above −1.38 unitswas 75.6% sensitive and 72.6% specificity at differentiating patientswith activity and non active disease (AUC=0.784).

TABLE 47 CBCAPs levels in SLE patients with non-active vs. activedisease A SLEDAI score ≥ 6 differentiated active from non activedisease. Results are expressed as median interquartile range. Non-activeActive disease disease N = 168 N = 41 P value ANA units/L 86 (33-131)125 (98-140) <0.001 units/L ≥ 90 47.3% 78.0% <0.001 EC4d Net MFI 11.3(7.2-19.5) 16.3 (11.3-26.0) 0.003 Net MFI > 14.8 33.7% 63.4% <0.001 BC4dNet MFI 66.2 (33.3-127.7) 117.0 (75.2-188.6) 0.004 Net MFI > 71.5 44.9%78.0% <0.001 PC4d Net MFI 5.0 (32.4-10.7) 13.9 (7.3-43.4) <0.001 NetMFI > 6.3 39.6% 78.0% <0.001 ECR1 Net MFI 13.5 (9.2-17.9) 9.2 (6.7-12.4)<0.001 Net MFI > 10.2 68.0% 34.1% <0.001 INDEX DIAGNOSTIC [ANA + EC4d +BC4d + anti-MCV] Index 1.74 (−0.02-2.92) 2.85 (1.81-3.63) <0.001 Index ≥0 74.4% 97.5% <0.001 Index > 1.36 46.4% 90.2% <0.001 ACTIVITY SCORE[ANA + PC4d + ECR1] Index −2.03 (−2.83; −1.23) −0.94 (−1.37; −0.16)<0.001 Index > 0 16.4% 64.2% <0.001 Index >− 1.38  7.5% 40.2% <0.001

What is claimed is:
 1. A method for treating systemic lupuserythematosus in a human subject in need thereof, wherein the humansubject is negative for systemic lupus erythematosus based on a level ofdouble-stranded DNA antibody; the method comprising: (a) measuring alevel of erythrocyte-bound complement component C4d (EC4d) marker in ablood sample obtained from the patient; (b) measuring a level ofB-lymphocyte-bound C4d complement component (BC4d) marker in the bloodsample; (c) measuring a level of anti-nuclear antibody (ANA) in theblood sample; (d) determining a systemic lupus erythematosus risk scorecomprising the steps of: (i) log normalizing the level of EC4d toproduce a log normalized level of EC4d, and multiplying the lognormalized level of EC4d by a predetermined weighting coefficient toproduce a weighted score for EC4d; (ii) log normalizing the level ofBC4d to produce a log normalized level of BC4d, and multiplying the lognormalized level of BC4d by a predetermined weighting coefficient toproduce a weighted score for BC4d; (iii) multiplying the level of ANA bya predetermined weighting coefficient to produce a weighted score forANA; and (iv) summing the weighted score for each of EC4d, BC4d, andANA, thereby determining the systemic lupus erythematosus risk score;and (e) determining that the human subject has systemic lupuserythematosus based on the systemic lupus erythematosus risk score; and(f) treating the human subject from step (e) that has systemic lupuserythematosus based on the systemic lupus erythematosus risk score withan effective amount of cyclophosphamide, a corticosteroid,mycophenolate, methotrexate, azathioprine, leflunomide, belimumab, orhydroxychloroquine.
 2. The method of claim 1, wherein step (a) comprisesmeasuring the level of EC4d marker by enzyme-linked immunosorbent assay,indirect immunofluorescence, radioimmunoassay, or flow cytometry; andstep (b) comprises measuring the level of BC4d marker by enzyme-linkedimmunosorbent assay, indirect immunofluorescence, radioimmunoassay, orflow cytometry.
 3. The method of claim 1, wherein step (c) comprisesmeasuring the level of ANA in the blood sample by enzyme-linkedimmunosorbent assay, indirect immunofluorescence, radioimmunoassay, orflow cytometry.
 4. The method of claim 1, wherein log in steps (d)(1)and (d)(2) is natural log.
 5. The method of claim 1, wherein step(d)(iii) comprises multiplying a number associated with a cutoff valuefor the level of ANA by a predetermined weighting coefficient to producea weighted score for ANA.
 6. The method of claim 1, wherein measuringthe level of EC4d marker comprises measuring the level of EC4d on thesurface of erythrocytes.
 7. The method of claim 1, wherein measuring thelevel of BC4d marker comprises measuring the level of BC4d on thesurface of B lymphocytes.
 8. The method of claim 1, wherein the humansubject has a level of EC4d marker that is less than 6 standarddeviations above the mean EC4d marker among non-lupus patients.
 9. Themethod of claim 1, wherein the human subject has a level of BC4d markerthat is less than 6 standard deviations above the mean BC4d marker amongnon-lupus patients.